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OBJECTIVE: To evaluate the discriminative ability and calibration of the RIETE, Kuijer, and HAS-BLED models for predicting 3-month bleeding risk in patients anticoagulated for venous thromboembolism (VTE). METHODS: External validation study of a prediction model based on a retrospective cohort of patients with VTE seen at the Hospital Universitario San Ignacio, Bogotá (Colombia) between July 2021 and June 2023. The calibration of the scales was evaluated using the Hosmer-Lemeshow test and the ratio of observed to expected events (ROE) within each risk category. Discriminatory ability was assessed using the area under the curve (AUC) of a ROC curve. RESULTS: We analyzed 470 patients (median age 65 years, female sex 59.3%) with a diagnosis of deep vein thrombosis in most cases (57.4%), 5.7% bleeding events were observed. Regarding calibration, adequate calibration cannot be ruled out given the limited number of events. The discriminatory ability was limited with an area under the curve (AUC) of 0.48 (CI 0.37-0.59) for Kuijer Score, 0.58 (CI 0.47-0.70) for HAS-BLED and 0.64 (CI 0.51-0.76) for RIETE. CONCLUSION: The Kuijer, HAS-BLED, and RIETE models in patients with VTE generally do not adequately estimate the risk of bleeding at three months, with a low ability to discriminate high-risk patients. Cautious interpretation is recommended until further evidence is available.

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Abstract Objective: To build a model based on cardiometabolic indicators that allow the identification of overweight adolescents at higher risk of subclinical atherosclerotic disease (SAD). Methods: Cross-sectional study involving 161 adolescents with a body mass index ≥ + 1 z-Score, aged 10 to 19 years. Carotid intima-media complex thickness (IMT) was evaluated using ultrasound to assess subclinical atherosclerotic disease. Cardiometabolic indicators evaluated included nutritional status, central adiposity, blood pressure, lipidic profile, glycemic profile, as well as age and sex. Data was presented using measures of central tendency and dispersion, as well as absolute and relative frequency. The relationship between IMT measurement (outcome variable) and other variables (independent variables) was assessed using Pearson or Spearman correlation, followed by multiple regression modeling with Gamma distribution to analyze predictors of IMT. Statistical analysis was performed using SPSS and R software, considering a significance level of 5 %. Results: It was observed that 23.7 % had Carotid thickening, and the prevalence of abnormal fasting glucose was the lowest. Age and fasting glucose were identified as predictors of IMT increase, with IMT decreasing with age by approximately 1 % per year and increasing with glucose by around 0.24 % per mg/dL. Conclusion: The adolescent at higher risk is younger with higher fasting glycemia levels.

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Temperate fruit and nut crops require distinctive cold and warm seasons to meet their physiological requirements and progress through their phenological stages. Consequently, they have been traditionally cultivated in warm temperate climate regions characterized by dry-summer and wet-winter seasons. However, fruit and nut production in these areas faces new challenging conditions due to increasingly severe and erratic weather patterns caused by climate change. This review represents an effort towards identifying the current state of knowledge, key challenges, and gaps that emerge from studies of climate change effects on fruit and nut crops produced in warm temperate climates. Following the PRISMA methodology for systematic reviews, we analyzed 403 articles published between 2000 and 2023 that met the defined eligibility criteria. A 44-fold increase in the number of publications during the last two decades reflects a growing interest in research related to both a better understanding of the effects of climate anomalies on temperate fruit and nut production and the need to find strategies that allow this industry to adapt to current and future weather conditions while reducing its environmental impacts. In an extended analysis beyond the scope of the systematic review methodology, we classified the literature into six main areas of research, including responses to environmental conditions, water management, sustainable agriculture, breeding and genetics, prediction models, and production systems. Given the rapid expansion of climate change-related literature, our analysis provides valuable information for researchers, as it can help them identify aspects that are well understood, topics that remain unexplored, and urgent questions that need to be addressed in the future.

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Wheat head blast is a major disease of wheat in the Brazilian Cerrado. Empirical models for predicting epidemics were developed using data from field trials conducted in Patos de Minas (2013 to 2019) and trials conducted across 10 other sites (2012 to 2020) in Brazil, resulting in 143 epidemics, with each being classified as either outbreak (≥20% head blast incidence) or nonoutbreak. Daily weather variables were collected from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) website and summarized for each epidemic. Wheat heading date (WHD) served to define four time windows, with each comprising two 7-day intervals (before and after WHD), which combined with weather-based variables resulted in 36 predictors (nine weather variables × four windows). Logistic regression models were fitted to binary data, with variable selection using least absolute shrinkage and selection operator (LASSO) and sequentially best subset analyses. The models were validated using the leave-one-out cross-validation (LOOCV) technique, and their statistical performance was compared. One model was selected, implemented in a 24-year series, and assessed by experts and literature. Models with two to five predictors showed accuracies between 0.80 and 0.85, sensitivities from 0.80 to 0.91, specificities from 0.72 to 0.86, and area under the curve (AUC) from 0.89 to 0.91. The accuracy of LOOCV ranged from 0.76 to 0.81. The model applied to a historical series included temperature and relative humidity in preheading date, as well as postheading precipitation. The model accurately predicted the occurrence of outbreaks, aligning closely with real-world observations, specifically tailored for locations with tropical and subtropical climates.

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INTRODUCTION: COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated. OBJECTIVE: To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population. METHODOLOGY: Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification. RESULTS: The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/). CONCLUSION: The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.

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OBJECTIVE: To build a model based on cardiometabolic indicators that allow the identification of overweight adolescents at higher risk of subclinical atherosclerotic disease (SAD). METHODS: Cross-sectional study involving 161 adolescents with a body mass index ≥ +1 z-Score, aged 10 to 19 years. Carotid intima-media complex thickness (IMT) was evaluated using ultrasound to assess subclinical atherosclerotic disease. Cardiometabolic indicators evaluated included nutritional status, central adiposity, blood pressure, lipidic profile, glycemic profile, as well as age and sex. Data was presented using measures of central tendency and dispersion, as well as absolute and relative frequency. The relationship between IMT measurement (outcome variable) and other variables (independent variables) was assessed using Pearson or Spearman correlation, followed by multiple regression modeling with Gamma distribution to analyze predictors of IMT. Statistical analysis was performed using SPSS and R software, considering a significance level of 5 %. RESULTS: It was observed that 23.7 % had Carotid thickening, and the prevalence of abnormal fasting glucose was the lowest. Age and fasting glucose were identified as predictors of IMT increase, with IMT decreasing with age by approximately 1 % per year and increasing with glucose by around 0.24 % per mg/dL. CONCLUSION: The adolescent at higher risk is younger with higher fasting glycemia levels.

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Gut microbiota has been implicated in various clinical conditions, yet the substantial heterogeneity in gut microbiota research results necessitates a more sophisticated approach than merely identifying statistically different microbial taxa between healthy and unhealthy individuals. Our study seeks to not only select microbial taxa but also explore their synergy with phenotypic host variables to develop novel predictive models for specific clinical conditions. DESIGN: We assessed 50 healthy and 152 unhealthy individuals for phenotypic variables (PV) and gut microbiota (GM) composition by 16S rRNA gene sequencing. The entire modeling process was conducted in the R environment using the Random Forest algorithm. Model performance was assessed through ROC curve construction. RESULTS: We evaluated 52 bacterial taxa and pre-selected PV (p < 0.05) for their contribution to the final models. Across all diseases, the models achieved their best performance when GM and PV data were integrated. Notably, the integrated predictive models demonstrated exceptional performance for rheumatoid arthritis (AUC = 88.03%), type 2 diabetes (AUC = 96.96%), systemic lupus erythematosus (AUC = 98.4%), and type 1 diabetes (AUC = 86.19%). CONCLUSION: Our findings underscore that the selection of bacterial taxa based solely on differences in relative abundance between groups is insufficient to serve as clinical markers. Machine learning techniques are essential for mitigating the considerable variability observed within gut microbiota. In our study, the use of microbial taxa alone exhibited limited predictive power for health outcomes, while the integration of phenotypic variables into predictive models substantially enhanced their predictive capabilities.

What is Already Known on this Subject? While the gut microbiota has been implicated as potential signatures or biomarkers for various clinical conditions, the establishment of causality in humans remains largely elusive.The role of the gut microbiota in maintaining the host organism's proper physiological function is well-established, yet data regarding the composition of the gut microbiota in disease states often suffer from poor reproducibility.What Are the New Findings? Our study demonstrates that relying solely on differences in the relative abundance of bacterial taxa between groups falls short as a means of identifying clinical markers.We advocate the use of robust statistical tools, such as bootstrapping, to mitigate the substantial variability observed in gut microbiota studies, thereby enhancing the reproducibility of research findings.Our findings underscore the limited predictive power of microbial taxa in isolation for health outcomes.The integration of phenotypic variables into predictive models with gut microbiota significantly augments the ability to predict health outcomes.How This Study Might Advance Research Despite the growing enthusiasm for using gut microbiota as biomarkers for various clinical conditions, the lack of standardization throughout the research process impedes progress in this field.Our study emphasizes the necessity of rigorously testing predictions of clinical conditions based on gut microbiota using bootstrapping techniques, promoting greater reproducibility in research findings.

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Abstract Introduction COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated. Objective To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population. Methodology Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification. Results The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/). Conclusion The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.

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Abstract Visceral leishmaniasis (VL) is an infectious disease predominant in countries located in the tropics. The prediction of occurrence of infectious diseases through epidemiologic modeling has revealed to be an important tool in the understanding of its occurrence dynamic. The objective of this study was to develop a forecasting model for the incidence of VL in Maranhão using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). We collected monthly data regarding VL cases from the National Disease Notification System (SINAN) corresponding to the period between 2001 and 2018. The Box-Jenkins method was applied in order to adjust a SARIMA prediction model for VL general incidence and by sex (male or female) for the period between January 2019 and December 2013. For 216 months of this time series, 10,431 cases of VL were notified in Maranhão, with an average of 579 cases per year. With regard to age range, there was a higher incidence among the pediatric public (0 to 14 years of age). There was a predominance in male cases, 6437 (61.71%). The Box-Pierce test figures for overall, male and female genders supported by the results of the Ljung-Box test suggest that the autocorrelations of residual values act as white noise. Regarding monthly occurrences in general and by gender, the SARIMA models (2,0,0) (2,0,0), (0,1,1) (0,1,1) and (0,1,1) (2, 0, 0) were the ones that mostly adjusted to the data respectively. The model SARIMA has proven to be an adequate tool for predicting and analyzing the trends in VL incidence in Maranhão. The time variation determination and its prediction are decisive in providing guidance in health measure intervention.

Resumo A leishmaniose visceral (LV) é uma doença de natureza infecciosa, predominante em países de zonas tropicais. A predição de ocorrência de doenças infecciosas através da modelagem epidemiológica tem se revelado uma importante ferramenta no entendimento de sua dinâmica de ocorrência. O objetivo deste estudo foi desenvolver um modelo de previsão da incidência da LV no Maranhão usando o modelo de Média Móvel Integrada Autocorrelacionada Sazonal (SARIMA). Foram coletados os dados mensais de casos de LV através do Sistema de Informação de Agravos de Notificação (SINAN) correspondentes ao período de 2001 a 2018. O método de Box-Jenkins foi aplicado para ajustar um modelo de predição SARIMA para incidência geral e por sexo (masculino e feminino) de LV para o período de janeiro de 2019 a dezembro de 2023. Durante o período de 216 meses dessa série temporal, foram registrados 10.431 casos de LV no Maranhão, com uma média de 579 casos por ano. Em relação à faixa etária, houve maior registro no público pediátrico (0 a 14 anos). Houve predominância do sexo masculino, com 6437 casos (61,71%). Os valores do teste de Box-Pierce para incidência geral, sexo masculino e feminino reforçados pelos resultados do teste Ljung-Box sugerem que as autocorrelações de resíduos apresentam um comportamento de ruído branco. Para incidência mensal geral e por sexo masculino e feminino, os modelos SARIMA (2,0,0) (2,0,0), (0,1,1) (0,1,1) e (0,1,1) (2, 0, 0) foram os que mais se ajustaram aos dados, respectivamente. O modelo SARIMA se mostrou uma ferramenta adequada de previsão e análise da tendência de incidência da LV no Maranhão. A determinação da variação temporal e sua predição são determinantes no norteamento de medidas de intervenção em saúde.

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Abstract Visceral leishmaniasis (VL) is an infectious disease predominant in countries located in the tropics. The prediction of occurrence of infectious diseases through epidemiologic modeling has revealed to be an important tool in the understanding of its occurrence dynamic. The objective of this study was to develop a forecasting model for the incidence of VL in Maranhão using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). We collected monthly data regarding VL cases from the National Disease Notification System (SINAN) corresponding to the period between 2001 and 2018. The Box-Jenkins method was applied in order to adjust a SARIMA prediction model for VL general incidence and by sex (male or female) for the period between January 2019 and December 2013. For 216 months of this time series, 10,431 cases of VL were notified in Maranhão, with an average of 579 cases per year. With regard to age range, there was a higher incidence among the pediatric public (0 to 14 years of age). There was a predominance in male cases, 6437 (61.71%). The Box-Pierce test figures for overall, male and female genders supported by the results of the Ljung-Box test suggest that the autocorrelations of residual values act as white noise. Regarding monthly occurrences in general and by gender, the SARIMA models (2,0,0) (2,0,0), (0,1,1) (0,1,1) and (0,1,1) (2, 0, 0) were the ones that mostly adjusted to the data respectively. The model SARIMA has proven to be an adequate tool for predicting and analyzing the trends in VL incidence in Maranhão. The time variation determination and its prediction are decisive in providing guidance in health measure intervention.

Resumo A leishmaniose visceral (LV) é uma doença de natureza infecciosa, predominante em países de zonas tropicais. A predição de ocorrência de doenças infecciosas através da modelagem epidemiológica tem se revelado uma importante ferramenta no entendimento de sua dinâmica de ocorrência. O objetivo deste estudo foi desenvolver um modelo de previsão da incidência da LV no Maranhão usando o modelo de Média Móvel Integrada Autocorrelacionada Sazonal (SARIMA). Foram coletados os dados mensais de casos de LV através do Sistema de Informação de Agravos de Notificação (SINAN) correspondentes ao período de 2001 a 2018. O método de Box-Jenkins foi aplicado para ajustar um modelo de predição SARIMA para incidência geral e por sexo (masculino e feminino) de LV para o período de janeiro de 2019 a dezembro de 2023. Durante o período de 216 meses dessa série temporal, foram registrados 10.431 casos de LV no Maranhão, com uma média de 579 casos por ano. Em relação à faixa etária, houve maior registro no público pediátrico (0 a 14 anos). Houve predominância do sexo masculino, com 6437 casos (61,71%). Os valores do teste de Box-Pierce para incidência geral, sexo masculino e feminino reforçados pelos resultados do teste Ljung-Box sugerem que as autocorrelações de resíduos apresentam um comportamento de ruído branco. Para incidência mensal geral e por sexo masculino e feminino, os modelos SARIMA (2,0,0) (2,0,0), (0,1,1) (0,1,1) e (0,1,1) (2, 0, 0) foram os que mais se ajustaram aos dados, respectivamente. O modelo SARIMA se mostrou uma ferramenta adequada de previsão e análise da tendência de incidência da LV no Maranhão. A determinação da variação temporal e sua predição são determinantes no norteamento de medidas de intervenção em saúde.

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The gastrointestinal tract (GIT) wet pool size (GITwps) refers to the total amount of wet contents in GIT, which in small ruminants can reach up to 19% of their body weight (BW). This study aimed to develop models to comprehensively predict GITwps in small ruminants using a meta-regression approach. A dataset was created based on 21 studies, comprising 750 individual records of sheep and goats. Various predictor variables, including BW, sex, breed, species, intake level, physiological states, stages and types of pregnancy, dry matter intake, and neutral detergent fiber intake (NDFI), were initially analyzed through simple linear regression. Subsequently, the variables were fitted using natural logarithm transformations, considering the random effect of the study and residual error, employing a supervised forward selection procedure. Overall, no significant relationship between GITwps and BW (p = 0.326) was observed for animals fed a milk-based diet. However, a strong negative linear relationship (p < 0.001) was found for animals on a solid diet, with the level of restriction influencing GITwps only at the intercept. Furthermore, the prediction of GITwps was independent of sex and influenced by species in cases where individuals were fed ad libitum. Pregnant females showed a noticeable reduction in GITwps, which was more pronounced in cases of multiple pregnancies, regardless of species (p < 0.01). The composition of the diet was found to be the primary factor affecting the modulation of GITwps, with NDFI able to override the species effect (p < 0.0001). Overall, this study sheds light on the factors influencing GITwps in small ruminants, providing valuable insights into their digestive processes and nutritional requirements.

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Our objective was to evaluate the productive parameters of ewes and their lambs in relation to ewe age and to develop models for predicting lamb weight at birth and weaning in a tropical warm-climate pasture. Individual data were collected from 273 ewes and 273 lambs, between 2013 and 2021. During this period all animals were kept on pastures of Brachiaria brizantha cv. Marandu All lambs in the experiments were supplemented in creep-feeding. A descriptive statistical analysis was performed using the PROC SUMMARY procedure in SAS (SAS University Edition, SAS Institute Inc. Cary, CA, USA). Pearson correlation coefficients between variables were estimated using the PROC CORR procedure in SAS (SAS University Edition, SAS Institute Inc. Cary, CA, USA). Model adjustments and variable selection were performed using PROC REG in SAS (SAS University Edition, SAS Institute Inc. Cary, CA, USA). The STEPWISE option and Mallow's C(p) were used to select the variables included in the equations. Outliers were identified by evaluating the studentized residuals based on the predicted values from the equations. Residual analysis was predicted by regression minus observed values and those that fell outside the range of -2.5 to 2.5 were removed. Several statistics were used to assess the predictability of the equations, including the coefficients of determination (r2) and mean standard error (RMSE). The average ewe age at lambing was 3.4 ± 1.7 years, with an average weight of 56.9 ± 8,9 kg and average body condition score (BCS) of 2,4 ± 0.8 points. The average ewe age at weaning was 51.1 ± 7.9, with average BCS of 1.8 ± 0.8 points. The average lamb at birthing was 3.9 ± 0.9 kg. The average lamb at weaning was 21.0 ± 4.9, with daily gain of 0.2 ± 0.1 kg/day and total gain of 17.1 ± 4.7 kg birth to weaning. The lamb produced by ewe at lambing was 5.3 ± 1.7 kg/ewe. The lamb weaned by ewe at weaning was 28.7 ± 10.8 kg/ewe. The ratios of lamb produced per ewe at birth and at weaning were 0.1 ± 0.03 and 0.6 ± 0.2, respectively. The lamb's birth weight showed a positive linear relationship with the age of the ewe, increasing by 115 g per year of age. The regression equations adjusted for ewe age had maximum points ranging from 4.2 to 5.2, occurring at average age of 4,7 years. The other characteristics showed a quadratic tendency. The results suggest the culling of ewes at five years of age to generate lambs with ideal weight at birth and at weaning raised in warm tropical pastures.

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Abstract Background: Assessment of animal growth based on live weight (LW) in traditional sheep production systems is limited by the high cost of purchase and maintenance of livestock scales. Objective: To develop and evaluate equations for LW prediction using heart girth (HG) in growing Pelibuey sheep. Methods: A dataset (n=415) of clinically healthy male Pelibuey sheep from two months to one year of age, with an average LW of 25.96 ± 10.25 kg and HG of 68.31 ± 10.53 cm, were used. Three equations were evaluated: LW (kg) = −37.70 + 0.93 × HG (Eq. 1); LW (kg) = −1.74 + 0.19 × HG + 0.008 × HG2 (Eq. 2); and LW (kg) = 0.003 × HG2.68 (Eq. 3). Results: The correlation coefficient between LW and HG was r = 0.94 (p<0.001). The three equations showed a high concordance correlation coefficient (CCCs≥0.97). However, the random error was the main component of the mean square partition of the prediction error (≥82.78%) only for Eqs. 1 and 2. The test for parameter identity (intercept=0; slope=1) was accepted only for Eq. 2 (p>0.05). On the other hand, for Eqs. 1 and 3 the intercept was different from zero and the slope was different from one (p<0.05). Conclusion: The second-degree equation accurately and precisely estimated body weight of growing Pelibuey sheep using the HG as a sole predictor variable.

Resumen Antecedentes : Debido a las condiciones de los sistemas tradicionales de producción ovina, la evaluación del crecimiento animal en función del peso vivo (PV) está limitada por el alto costo de la báscula ganadera y su mantenimiento. Objetivo: Desarrollar y evaluar ecuaciones para predecir el peso corporal utilizando el perímetro torácico (PT) en ovinos Pelibuey en crecimiento. Métodos : Se utilizó un conjunto de datos (n=415) de ovinos Pelibuey machos clínicamente sanos, de dos meses a un año de edad y peso promedio de 25,96 ± 10,25 kg y PT de 68,31 ± 10,53 cm. Se evaluaron tres ecuaciones: PV (kg) = −37,70 + 0,93 × PT (Ec. 1), PV (kg) = −1,74 + 0,19 × PT + 0,008 × PT2 (Ec. 2) y PV (kg) = 0,003 × PT2,68 (Ec. 3). Resultados: El coeficiente de correlación entre PV y PT fue r=0,94 (p<0,001). Las tres ecuaciones mostraron alto coeficiente de correlación de concordancia (CCCs≥0,97). Sin embargo, el error aleatorio fue el componente principal de la partición cuadrática media del error de predicción (≥82,78%) solo para las Ecs. 1 y 2. Sin embargo, la prueba de identidad de parámetros (intersección = 0; pendiente = 1) solo se aceptó para la ecuación 2 (p>0,05). Por otro lado, el intercepto fue diferente de cero y la pendiente fue diferente de uno (p<0.05) para las Ecs. 1 y 3. Conclusión: La ecuación de segundo grado estima con exactitud y precisión el peso corporal de ovinos Pelibuey en crecimiento utilizando la PT como única variable predictora.

Resumo Antecedentes: Devido às condições dos sistemas tradicionais de produção de ovinos, a avaliação do crescimento animal com base no peso corporal (PV) é limitada pelo alto custo da balança pecuária, bem como pela manutenção sofisticada necessária. Objetivo: Desenvolver e avaliar equações para predizer o PV usando o perímetro torácico (PT) em ovinos Pelibuey em crescimento. Métodos: Um conjunto de dados (n=415) de ovinos Pelibuey machos clinicamente saudáveis de dois meses a um ano de idade, com peso médio de 25,96 ± 10,25 kg e PT de 68,31 ± 10,53 cm foi utilizado para o desenvolvimento das equações. Três equações foram avaliadas: PV (kg) = -37,70 + 0,93 × PT (Eq. 1), PV (kg) = -1,74 + 0,19 × PT + 0,008 × PT2 (Eq. 2) e PV (kg) = 0,003 × PT2,68 (Eq. 3). Resultados: O coeficiente de correlação entre PV e PT foi r = 0,94 (P < 0,001). As três equações apresentaram alto coeficiente de correlação e concordância (CCCs≥0,97). No entanto, o erro aleatório foi o principal componente da partição do quadrado médio do erro de predição (≥82,78%) apenas para as Eqs. 1 e 2. No entanto, o teste de identidade dos parâmetros (intercepto = 0; inclinação = 1) foi aceito apenas para a Eq. 2 (p>0,05). Por outro lado, para a Eq. 1 e 3, o intercepto foi diferente de zero e a inclinação foi diferente de um (p<0,05). Conclusões: A equação de segundo grau estima com precisão e acurácia o peso corporal de ovinos Pelibuey em crescimento usando o PT como única variável preditora.

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Top-quality pellets can significantly increase density and durability of agricultural residues, reducing logistic costs. However, these pellets depend on numerous parameters, including feedstock properties and production conditions. To ensure high-quality pellets, a single-factor experiment and the response surface methodology were used to investigate the effects of particle size, moisture content, molding pressure, pelleting time, ultrasonic power, and interaction effects between variables on density and durability of pellets for ultrasonic vibration-assisted pelleting of corn stover. The response surface models between variables and response were established. The results showed that all variables affect the density and durability of pellets. An optimal condition for density and durability was obtained, and a further experiment was conducted to validate the values. The results suggested that desirability (0.999) under optimal conditions confirmed the validation of models. The optimal combination of process parameters included particle size of 1.5 mm, moisture content of 10 %, molding pressure of 379 kPa, pelleting time of 80 s, ultrasonic power of 250 W, with values of 1,381.14 kg m-3 and 97.58 % for density and durability of pellets, respectively.

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Introduction: The prediction of potential fishing areas is considered one of the most immediate and practical approaches in fisheries and is an essential technique for decision-making in managing fishery resources. It helps fishermen reduce their fuel costs and the uncertainty of their fish catches; this technique allows to contribute to national and international food security. In this study, we build different combinations of predictive statistical models such as Generalized Linear Models and Generalized Additive Models. Objective: To predict the spatial distribution of PFZs of the dolphinfish (Coryphaena hippurus L.) in the Colombian Pacific Ocean. Methods: We built different combinations of Generalized Linear Models and Generalized Additive Models to predict the Catch Per Unit Effort of C. hippurus captured from 2002 to 2015 as a function of sea surface temperature, chlorophyll-a concentration, sea level anomaly, and bathymetry. Results: A Generalized Additive Model with Gaussian error distribution obtained the best performance for predicting PFZs for C. hipurus. Model validation was performed by calculating the Root Mean Square Error through a cross-validation approach. The R2 of this model was 50 %, which was considered suitable for the type of data used. January and March were the months with the highest Catch per Unit Effort values, while November and December showed the lower values. Conclusion: The predicted PFZs of C. hippurus with Generalized Additive Models satisfactorily with the results of previous research, suggesting that our model can be explored as a tool for the assessment, decision making, and sustainable use of this species in the Colombian Pacific Ocean.

Introducción: La predicción de zonas potenciales de pesca se considera uno de los enfoques más inmediatos y efectivos en las pesquerías, es una técnica importante para la toma de decisiones en el manejo de los recursos pesqueros. Ayuda a los pescadores a reducir su costo de combustible y también a disminuir la incertidumbre de sus capturas, esta técnica permite contribuir a la seguridad alimentaria nacional e internacional. En este estudio, se construyeron diferentes combinaciones de modelos estadísticos predictivos como modelos lineales generalizados y modelos aditivos generalizados. Objetivo: predecir la distribución espacial de las zonas potenciales de pesca del pez dorado (Coryphaena hippurus L.) en el Pacífico colombiano. Métodos: La variable de respuesta se expresó en escala de captura por unidad de esfuerzo, es decir, el número de individuos de C. hippurus capturados por un número total de anzuelos disponibles entre 2002 y 2015. Temperatura de la superficie del mar, concentración de clorofila, anomalía del nivel del mar y batimetría, se utilizaron como variables explicativas para los meses de estacionalidad de C. hippurus (noviembre - marzo). Resultados: El modelo con mejor rendimiento para la predicción de zonas potenciales de pesca fue un modelo aditivo generalizado con distribución de error gaussiana y función de enlace de registro, que se seleccionó en función del criterio de información de Akaike, el R2 y la desviación explicada. La validación del modelo se realizó calculando el error cuadrático medio a través de un enfoque de validación cruzada. El ajuste de este modelo fue del 50 %, lo que puede considerarse adecuado para el tipo de datos utilizados. Enero y marzo fueron los meses con mayor captura por unidad de esfuerzo y noviembre-diciembre los meses con menor. Conclusión: Las zonas potenciales de pesca previstas coincidieron satisfactoriamente con investigaciones anteriores, lo que sugiere que nuestro modelo es una herramienta poderosa para la evaluación, toma de decisiones y uso sostenible de los recursos pesqueros de C. hippurus en el Pacífico colombiano.

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Abstract Objective: To explore the diagnostic utility of 31 electrocardiogram (ECG) criteria for detecting echocardiographic (Echo) left ventricular geometry using accuracy. Methods: This cross-sectional study included consecutive adults (> 18 years) that were classified by Echo left ventricular geometry as normal (NL), concentric remodeling (CR), concentric hypertrophy (CH), and eccentric hypertrophy (EH). Thirty-one state-of-the-art ECG criteria for Echo left ventricular hypertrophy were calculated. AUC 95%CI, accuracy, sensitivity, specificity, and positive and negative predictive value for detecting Echo left ventricular geometries were compared. Multivariable linear regression models were produced using the ECG criteria as the dependent variable. Results: A total of 672 adults were included in the study. From 31 ECG criteria, Cornell (ECG21, SV3 + RaVL) and modified Cornell (ECG 31, RaVL + deepest S in all leads) criteria have the best overall AUC in differentiating NL versus CH (0.666 and 0.646), NL versus EH (0.686 and 0.656), CR versus CH (0.687 and 0.661), and CR versus EH (0.718 and 0.676). In multivariable linear regression models, CH and EH had the strongest effect on the final voltage in Cor- nell (ECG21) and modified Cornell (ECG31). Conclusions: From 31 state-of-the-art criteria, Cornell and modified Cornell criteria have the best AUC and accuracy for predicting most left ventricular geometries. CH and EH had the strongest effect on the voltage of Cornell and modified Cornell criteria compared to body mass index, age, diabetes, hypertension, and chronic heart disease. The ECG criteria poorly differentiate NL from CR and CH from EH.

Resumen Objetivo: Explorar la utilidad diagnóstica de 31 criterios de ECG para detectar la geometría ecocardiográfica del ventrículo izquierdo usando la exactitud, área bajo la curva, sensibilidad, especificidad, y valor predictivo positivo y negativo. Métodos: Este estudio transversal incluyó adultos (> 18 años) que se sometieron a ECG y ecocardiograma transtorácico. Los pacientes fueron clasificados según la geometría del ventrículo izquierdo: normal (NL), remodelado concéntrico (RC), hipertrofia concéntrica (HC) e hipertrofia excéntrica (HE). Se calcularon 31 criterios clásicos de ECG para detectar hipertrofia ventricular izquierda y se comparó el rendimiento diagnóstico en cada geometría. Creamos un modelo de regresión lineal múltiple usando los criterios de ECG como variable dependiente. Resultados: Se incluyeron 672 adultos. Los criterios de Cornell (ECG 21, SV3 + RaVL) y Cornell modificado (ECG31, RaVL + S mas profunda de las 12 derivaciones) tienen el mejor AUC para diferenciar NL versus HC (0.666 y 0.646), NL versus HE (0.686 y 0.656), RC versus HC (0.687 y 0.661) y RC versus HE (0.718 y 0.676). En el análisis multivariado la geometría del ventrículo izquierdo (HC e HE) fue la variable que mas influyó en el resultado final del criterio de Cornell y de Cornell modificado. Conclusión: De los 31 criterios clásicos explorados, los criterios de Cornell y Cornell modificado tienen el mejor AUC y exactitud para predecir la mayoría de las geometrías del ventrículo izquierdo. Los criterios del ECG no diferencian bien la geometría NL del RC ni HC de la HE.

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OBJECTIVE: To explore the diagnostic utility of 31 electrocardiogram (ECG) criteria for detecting echocardiographic (Echo) left ventricular geometry using accuracy. METHODS: This cross-sectional study included consecutive adults (> 18 years) that were classified by Echo left ventricular geometry as normal (NL), concentric remodeling (CR), concentric hypertrophy (CH), and eccentric hypertrophy (EH). Thirty-one state-of-the-art ECG criteria for Echo left ventricular hypertrophy were calculated. AUC 95%CI, accuracy, sensitivity, specificity, and positive and negative predictive value for detecting Echo left ventricular geometries were compared. Multivariable linear regression models were produced using the ECG criteria as the dependent variable. RESULTS: A total of 672 adults were included in the study. From 31 ECG criteria, Cornell (ECG21, SV3 + RaVL) and modified Cornell (ECG 31, RaVL + deepest S in all leads) criteria have the best overall AUC in differentiating NL versus CH (0.666 and 0.646), NL versus EH (0.686 and 0.656), CR versus CH (0.687 and 0.661), and CR versus EH (0.718 and 0.676). In multivariable linear regression models, CH and EH had the strongest effect on the final voltage in Cor- nell (ECG21) and modified Cornell (ECG31). CONCLUSIONS: From 31 state-of-the-art criteria, Cornell and modified Cornell criteria have the best AUC and accuracy for predicting most left ventricular geometries. CH and EH had the strongest effect on the voltage of Cornell and modified Cornell criteria compared to body mass index, age, diabetes, hypertension, and chronic heart disease. The ECG criteria poorly differentiate NL from CR and CH from EH.

OBJETIVO: Explorar la utilidad diagnóstica de 31 criterios de ECG para detectar la geometría ecocardiográfica del ventrículo izquierdo usando la exactitud, área bajo la curva, sensibilidad, especificidad, y valor predictivo positivo y negativo. ­. MÉTODOS: Este estudio transversal incluyó adultos (> 18 años) que se sometieron a ECG y ecocardiograma transtorácico. Los pacientes fueron clasificados según la geometría del ventrículo izquierdo: normal (NL), remodelado concéntrico (RC), hipertrofia concéntrica (HC) e hipertrofia excéntrica (HE). Se calcularon 31 criterios clásicos de ECG para detectar hipertrofia ventricular izquierda y se comparó el rendimiento diagnóstico en cada geometría. Creamos un modelo de regresión lineal múltiple usando los criterios de ECG como variable dependiente. RESULTADOS: Se incluyeron 672 adultos. Los criterios de Cornell (ECG 21, SV3 + RaVL) y Cornell modificado (ECG31, RaVL + S mas profunda de las 12 derivaciones) tienen el mejor AUC para diferenciar NL versus HC (0.666 y 0.646), NL versus HE (0.686 y 0.656), RC versus HC (0.687 y 0.661) y RC versus HE (0.718 y 0.676). En el análisis multivariado la geometría del ventrículo izquierdo (HC e HE) fue la variable que mas influyó en el resultado final del criterio de Cornell y de Cornell modificado. CONCLUSIÓN: De los 31 criterios clásicos explorados, los criterios de Cornell y Cornell modificado tienen el mejor AUC y exactitud para predecir la mayoría de las geometrías del ventrículo izquierdo. Los criterios del ECG no diferencian bien la geometría NL del RC ni HC de la HE.

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Resumen Introducción: la identificación de los pacientes con mayor riesgo de progresar a falla renal es fundamental para la planeación del tratamiento en la enfermedad renal crónica, pero no ha podido llevarse a cabo consistentemente. Los modelos de predicción podrían ser una herramienta útil, sin embargo, su usabilidad en la Enfermedad Renal Crónica es limitada hasta ahora y no se comprenden muy bien las barreras y limitaciones. Métodos: se desarrolló una revisión de alcance de la literatura disponible sobre modelos predictivos de falla renal o reglas de pronóstico en pacientes con Enfermedad Renal Crónica. Las búsquedas se realizaron sistemáticamente en Cochrane, Pubmed y Embase. Se realizó una revisión ciega e independiente por dos evaluadores para identificar estudios que informaran sobre el desarrollo, la validación o la evaluación del impacto de un modelo construido para predecir la progresión al estadio avanzado de la enfermedad renal crónica. Se realizó una evaluación crítica de la calidad de la evidencia proporcionada con el sistema GRADE (Grading of Recommendations Assessment, Development and Evaluation). Resultados: de 1279 artículos encontrados, fueron incluidos 19 estudios para la síntesis cualitativa final. La mayoría de los estudios eran primarios, con diseños observacionales retrospectivos y unos pocos correspondieron a revisiones sistemáticas. No se encontraron guías de práctica clínica. La síntesis cualitativa evidenció gran heterogeneidad en el desarrollo de los modelos, así como en el reporte de las medidas de desempeño global, la validez interna y la falta de validez externa en la mayoría de los estudios. La calificación de la evidencia arrojó una calidad global baja, con inconsistencia entre los estudios e importantes limitaciones metodológicas. Conclusiones: la mayoría de los modelos predictivos disponibles, no han sido adecuadamente validados y, por tanto, se consideran de uso limitado para evaluar el pronóstico individual del paciente con enfermedad renal crónica. Por lo tanto, se requieren esfuerzos adicionales para centrar el desarrollo e implementación de modelos predictivos en la validez externa y la usabilidad y disminuir la brecha entre la generación, la síntesis de evidencia y la toma de decisiones en el ámbito del cuidado del paciente.

Abstract Background: the identification of patients at higher risk of progressing to kidney failure is essential for treatment planning in chronic kidney disease, but it has not been possible to do this consistently. Predictive models could be a useful tool, however, their usability in chronic kidney disease is limited and the barriers and limitations are not well understood. Methods: a scoping review of the available literature on ESRD predictive models or prognostic rules in chronic kidney disease patients was developed. Searches were systematically executed on Cochrane, MEDLINE, and Embase. a blind and independent review was carried out by two evaluators to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the progression to an advanced stage of chronic kidney disease. A critical evaluation of the quality of the evidence provided with the GRADE system (Grading of Recommendations Assessment, Development and Evaluation) was made. Findings: of 1279 articles found, 19 studies were included for the final qualitative synthesis. Most of the studies were primary, with retrospective observational designs and a few corresponded to systematic reviews. No clinical practice guidelines were found. The qualitative synthesis showed high heterogeneity in the development of the models, as well as in the reporting of global performance measures, internal validity, and the lack of external validity in most of the studies. The evidence rating was of low overall quality, with inconsistency between studies and important methodological limitations. Conclusions: most of the available predictive models have not been adequately validated and, therefore, are of limited use to assess the individual prognosis of patients with chronic kidney disease. Therefore, additional efforts are required to focus the development and implementation of predictive models on external validity and usability and bridge the gap between generation, synthesis of evidence, and decision-making in the field of patient care.

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Due to the conditions in which traditional sheep production systems operate, the evaluation of animal growth from live weight (LW) is limited by the high cost of the livestock scale as well as the sophisticated maintenance required. In this scenario, in recent years, biometric measurements have been investigated as an accurate indirect method to predict the LW of farm animals. Therefore, the present study was undertaken to examine different models for predicting the body weight of growing lambs using the body volume (BV) formula. Body volume, heart girth (HG) and body length (BL) data of 290 lambs aged between two and eight months were recorded. Body volume was calculated from HG and BL data using a formula that calculates the volume of a cylinder. The estimation of LW from the BV formula was achieved through regression equations using three mathematical models (linear, quadratic and exponential). The mean values of LW, HG, BL and BV of the lambs were 29.12±12.04kg, 70.00±11.69cm, 38.40±6.43cm and 23.93±9.90dm3, respectively. The correlation coefficient between LW and BV was r = 0.96 (P<0.001). The quadratic model showed the highest coefficient of determination (0.93) and the lowest prediction error (3.29kg). Under the experimental conditions adopted in this study, it is possible to predict the live weight of growing lambs using the body volume formula.

Devido às condições dos sistemas tradicionais de produção de ovinos, a avaliação do crescimento animal a partir do peso vivo (PV) é limitada pelo alto custo da balança pecuária, bem como pela sofisticada manutenção necessária. Assim, nos últimos anos, as medidas biométricas (MB) têm sido avaliadas como um método indireto e preciso para predizer o PV de animais de criação. Portanto, o objetivo desta pesquisa foi avaliar diferentes modelos de predição do PV de cordeiros em crescimento utilizando-se a fórmula do volume corporal (VC). Foram registrados dados de PV, perímetro torácico (PT) e comprimento corporal (CC) de 290 cordeiros entre dois e oito meses de idade. O VC foi calculado com base nos dados PT e CC, sendo usada uma fórmula que calcula o volume de um cilindro. A previsão do PV a partir da fórmula VC foi estimada por meio de equações de regressão, utilizando-se três modelos matemáticos (linear, quadrático e exponencial). Os valores médios do PV, PT, CC e VC dos cordeiros foram 29,12±12,04kg, 70,00±11,69cm, 38,40±6,43cm e 23,93±9,90 (dm3), respectivamente. O coeficiente de correlação entre PV e VC foi r=0,96 (P<0,001). O modelo quadrático apresentou o maior coeficiente de determinação (0,93) e o menor erro de predição (3,29kg) Nas condições do presente estudo, conclui-se que é possível predizer o peso vivo de cordeiros em crescimento por meio da fórmula de volume corporal.

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BACKGROUND: The frequency of cancer and accuracy of prediction models have not been studied in large, population-based samples of patients with incidental pulmonary nodules measuring > 8 mm in diameter. RESEARCH QUESTIONS: How does the frequency of cancer vary by size and smoking history among patients with incidental nodules? How accurate are two widely used models for identifying cancer in these patients? STUDY DESIGN AND METHODS: We assembled a retrospective cohort of individuals with incidental nodules measuring > 8 mm in diameter identified by chest CT imaging between 2006 and 2016. We used a validated natural language processing algorithm to identify nodules and their characteristics by scanning the text of dictated radiology reports. We reported patient and nodule characteristics stratified by the presence or absence of a lung cancer diagnosis within 27 months of nodule identification and estimated the area under the receiver operating characteristic curve (AUC) to compare the accuracy of the Mayo Clinic and Brock models for identifying cancer. RESULTS: The sample included 23,780 individuals with a nodule measuring > 8 mm, including 2,356 patients (9.9%) with a lung cancer diagnosis within 27 months of nodule identification. Cancer was diagnosed in 5.4% of never smokers, 12.2% of former smokers, and 17.7% of current smokers. Cancer was diagnosed in 5.7% of patients with nodules measuring 9 to 15 mm, 12.1% of patients with nodules > 15 to 20 mm, and 18.4% of patients with nodules > 20 to 30 mm. In the full sample, the Mayo Clinic model (AUC, 0.747; 95% CI, 0.737-0.757) was more accurate than the Brock model (AUC, 0.713; 95% CI, 0.702-0.724; P < .0001). When restricted to ever smokers, the Mayo Clinic model was still more accurate. Both models overestimated the probability of cancer. INTERPRETATION: Almost 10% of patients with an incidental pulmonary nodule measuring > 8 mm in diameter will receive a lung cancer diagnosis. Existing prediction models have only fair accuracy and overestimate the probability of cancer.

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