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Understanding factors that influence the efficacy of functional communication training has both practical and conceptual benefits. The current study extended research in this area by exploring data from 95 consecutive applications of functional communication training with extinction across two independent clinics. We selected candidate predictor variables based on conceptual analysis, conducted preliminary exploratory analyses, and then selectively applied quantitative methods that are used in precision medicine to examine their accuracy and predictive utility. Treatment outcomes were better when challenging behavior was maintained by a single function than they were when it was maintained by multiple functions; however, these differences were most apparent among cases with an escape function. We also analyzed within-session responding to explore the potential influence of unprogrammed establishing operations on decrements in treatment efficacy. Our within-session measure only distinguished responders from nonresponders when escape was one of the multiple functions. Additional research is needed to validate these findings with an independent sample and to address a number of clinical conceptual issues.

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Data analytics and artificial intelligence (AI) have been used to predict patient outcomes after colorectal cancer surgery. A prospectively maintained colorectal cancer database was used, covering 4336 patients who underwent colorectal cancer surgery between 2003 and 2019. The 47 patient parameters included demographics, peri- and post-operative outcomes, surgical approaches, complications, and mortality. Data analytics were used to compare the importance of each variable and AI prediction models were built for length of stay (LOS), readmission, and mortality. Accuracies of at least 80% have been achieved. The significant predictors of LOS were age, ASA grade, operative time, presence or absence of a stoma, robotic or laparoscopic approach to surgery, and complications. The model with support vector regression (SVR) algorithms predicted the LOS with an accuracy of 83% and mean absolute error (MAE) of 9.69 days. The significant predictors of readmission were age, laparoscopic procedure, stoma performed, preoperative nodal (N) stage, operation time, operation mode, previous surgery type, LOS, and the specific procedure. A BI-LSTM model predicted readmission with 87.5% accuracy, 84% sensitivity, and 90% specificity. The significant predictors of mortality were age, ASA grade, BMI, the formation of a stoma, preoperative TNM staging, neoadjuvant chemotherapy, curative resection, and LOS. Classification predictive modelling predicted three different colorectal cancer mortality measures (overall mortality, and 31- and 91-days mortality) with 80-96% accuracy, 84-93% sensitivity, and 75-100% specificity. A model using all variables performed only slightly better than one that used just the most significant ones.

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Land use regression (LUR) models have been extensively used to predict air pollution exposure in epidemiological and environmental studies. The lack of dense routine monitoring networks in big cities places increased emphasis on the need for LUR models to be developed using purpose-designed neighborhood-scale monitoring data. However, the unsatisfactory model transferability limits these neighborhood LUR models to be then applied to other intra-urban areas in predicting air pollution exposure. In this study, we tackled this issue by proposing a method to develop transferable neighborhood NO2 LUR models with comparable predictive power based on only micro-scale predictor variables for modeling intra-urban ambient air pollution exposure. Taking Auckland metropolis, New Zealand, as a case study, the proposed method was applied to three neighborhoods (urban, central business district, and dominion road) and compared with the corresponding counterpart models developed using pools of (a) only macro-scale predictor variables and (b) a mixture of both micro- and macro-scale predictor variables (traditional method). The results showed that the models using only macro-scale variables achieved the lowest accuracy (R2: 0.388-0.484) and had the worst direct (R2: 0.0001-0.349) and indirect transferability (R2: 0.07-0.352). Those models using the traditional method had the highest model fitting R2 (0.629-0.966) with lower cross-validation R2 (0.495-0.941) and slightly better direct transferability (R2: 0.0003-0.386) but suffered poor model interpretability when indirectly transferred to new locations. Our proposed models had comparable model fitting R2 (0.601-0.966) and the best cross-validation R2 (0.514-0.941). They also had the strongest direct transferability (R2: 0.006-0.590) and moderate-to-good indirect transferability (R2: 0.072-0.850) with much better model interpretability. This study advances our knowledge of developing transferable LUR models for the very first time from the perspective of the scale of the predictor variables used in the model development and will significantly benefit the wider application of LUR approaches in epidemiological and environmental studies.

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INTRODUCTION: Although suicide is the main cause of maternal death during pregnancy in industrialized countries, there are few research regarding the prevalence and risk factors of suicidal ideation during pregnancy, especially in Spain. METHOD: In a multicenter study, the sample included 1,524 pregnant women recruited from an obstetrics setting from two Spanish tertiary-care public hospitals. The prevalence of prenatal suicidal ideation was estimated by analyzing their responses to item 9 on the Patient Health Questionnaire (PHQ-9). The risk factors (which increases the probability of having suicidal ideation) included sociodemographic and biomedical variables, and the stress subscale from the revised prenatal version of the Postpartum Depression Predictors Inventory (PDPI-R). RESULTS: A total of 2.6% of pregnant women reported suicidal ideation. Risk factors of suicidal ideation during pregnancy include sociodemographic, such as prior history of depression (ß = 0.120, p < .05), unemployment (ß = 0.149, p < .05), and being an immigrant (ß = 0.140, p < .01), and biomedical variables, such as previous abortion (ß =0.169, p < .01) and assisted reproduction (ß = -0.100, p < .05). DISCUSSION: Given that the prevalence of suicidal ideation is higher than expected, the results of the study suggest the critical need for screening and designing preventive interventions adapted to pregnant women to decrease risk of associated suicidal behavior. In protocols carried out by midwives, specific risk factors should be included in health screenings during pregnancy.

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Objective. Studies have examined possible predictors of success on the North American Pharmacist Licensure Examination (NAPLEX). This systematic review investigated the literature on potential predictors of success on the NAPLEX.Findings. Articles were included in the review if they studied student characteristics and academic performance as independent variables and NAPLEX scores and/or pass rates as an outcome. Data were extracted from each article for students' demographics or variables, sample size, methods of statistical analyses, and results reporting correlation or predictability. From 40 articles retrieved from the initial search and sorting, 20 studies were included in the final review per inclusion criteria. Three studies included all the pharmacy programs, 15 were single-institution studies, two were multi-institution studies, and four had been published as posters. Among 30 different variables identified as potential predictors of success on the NAPLEX, the most examined variables were student age at matriculation, having a prior degree, Pharmacy College Admission Test (PCAT) scores, cumulative pharmacy school grade point average (GPA), overall Pharmacy Curriculum Outcomes Assessment (PCOA) scores, and PCOA content areas scores. Positively correlated factors included PCAT scores, not having a prior degree, prepharmacy and pharmacy school GPA, institutional characteristics, and PCOA scores. Negatively correlated factors included older age at admission.Summary. Cumulative pharmacy school GPA and PCOA scores were predictors of NAPLEX success consistently in the studies. The effects of preadmission student characteristics on NAPLEX success varied and were not consistently correlated or predictive.

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Sensor technology was introduced to intraoperatively analyse the differential pressure between the medial and lateral compartments of the knee during primary TKA using a sensor to assess if further balancing procedures are needed to achieve a "balanced" knee. The prognostic role of epidemiological and radiological parameters was also analysed. A consecutive series of 21 patients with primary knee osteoarthritis were enrolled and programmed for TKA in our unit between 1 September 2020 and 31 March 2021. The VERASENSE Knee System (OrthoSensor Inc., Dania Beach, FL, USA) has been proposed as an instrument that quantifies the differential pressure between the compartments of the knee intraoperatively throughout the full range of motion during primary TKA, designed with a J-curve anatomical femoral design and a PS "medially congruent" polyethylene insert. Thirteen patients (61.90%) showed a "balanced" knee, and eight patients (38.10%) showed an intra-operative "unbalanced" knee and required additional procedures. A total of 13 additional balancing procedures were performed. At the end of surgical knee procedures, a quantitatively balanced knee was obtained in all patients. In addition, a correlation was found between the compartment pressure of phase I and phase II at 10° of flexion and higher absolute pressures were found in the medial compartment than in the lateral compartment in each ROM degree investigated. Moreover, those pressure values showed a trend to decrease with the increase in flexion degrees in both compartments. The "Kinetic Tracking" function displays the knee's dynamic motion through the full ROM to evaluate joint kinetics. The obtained kinetic traces reproduced the knee's medial pivot and femoral rollback, mimicking natural knee biomechanics. Moreover, we reported a statistically significant correlation between the need for soft tissue or bone resection rebalancing and severity of the initial coronal deformity (>10°) and a preoperative JLCA value >2°. The use of quantitative sensor-guided pressure evaluation during TKA leads to a more reproducible "balanced" knee. The surgeon, evaluating radiological parameters before surgery, may anticipate difficulties in knee balance and require those devices to achieve the desired result objectively.

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The coinfection of swine influenza (SI) strains and avian/human-source influenza strains in piggeries can contribute to the evolution of new influenza viruses with pandemic potential. This study analyzed surveillance data on SI in south China and explored the spatial predictor variables associated with different influenza infection scenarios in counties within the study area. Blood samples were collected from 7670 pigs from 534 pig farms from 2015 to 2017 and tested for evidence of infection with influenza strains from swine, human and avian sources. The herd prevalences for EA H1N1, H1N1pdm09, classic H1N1, HS-like H3N2, seasonal human H1N1 and avian influenza H9N2 were 88.5, 64.5, 60.3, 57.8, 12.9 and 10.3 %, respectively. Anthropogenic factors including detection frequency, chicken density, duck density, pig density and human population density were found to be better predictor variables for three influenza infection scenarios (infection with human strains, infection with avian strains, and coinfection with H9N2 avian strain and at least one swine strain) than were meteorological and geographical factors. Predictive risk maps generated for the four provinces in south China highlighted that the areas with a higher risk of the three infection scenarios were predominantly clustered in the delta area of the Pearl River in Guangdong province and counties surrounding Poyang Lake in Jiangxi province. Identification of higher risk areas can inform targeted surveillance for influenza in humans and pigs, helping public health authorities in designing risk-based SI control strategies to address the pandemic influenza threat in south China.

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INTRODUCTION: Up to 25% of acetabular fractures have poor functional outcomes in short-term follow-up. The aim of our study is to analyze predictors related to poor outcome in surgically treated acetabular fractures. Damage to the femoral head cartilage and poor fracture reduction has been reported as predictors to total hip arthroplasty (THA). MATERIAL AND METHODS: retrospective study of 207 consecutive patients with acetabular fractures, over a fourteen-year period. Demographic data, fracture pattern according to AO/OTA, complications related to surgery and predictor variables were analyzed. RESULTS: Analyzing predictor variables, we observed seagull sign, femoral head dislocation, femoral osteochondral damage, acetabular marginal impaction, poor acetabular roof congruency after surgery (p < 0.001) and postoperative fracture congruence (>3mm) (p < 0.023) statistically related to the need of a THA during follow-up. Age (p = 0,98), Sex(p = 0,27), AO-OTA classification (p = 0,10), type of dislocation (p = 0,25), surgical approach (p = 0,57), time to surgery (p = 0,66) and posterior wall involvement (p = 0,06) were not related to THA. Most frequent complication was nerve injury, affecting 22 patients (20.18%). Only seventeen patients (15.6%) needed a THA at an average time of 6 years after initial open reduction and internal fixation. CONCLUSION: Femoral head damage and dislocation, fracture reduction, and seagull sign were the strongest predictors related to THA after surgical treatment of acetabular fractures.

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The potential applicability of concepts and methods of the paradigm of precision medicine to the field of applied behavior analysis is only beginning to be explored. Both precision medicine and applied behavior analysis seek to understand and classify clinical problems through identification of their causal pathways. Both aim to develop treatments directly targeting those causal pathways, which also requires an understanding of the mechanisms by which treatments produce change (treatment-action pathways). In the current study, we extend the data-analytic methods and concepts described by Hagopian et al. (2018) toward the identification of variables that predict response to functional communication training (FCT). We discuss emerging conceptual issues, including the importance of distinguishing predictive behavioral markers from predictor variables based on their purported involvement in the causal or treatment-action pathways. Making these discriminations is a complex undertaking that requires knowledge of these mechanisms and how they interact.

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The aim is to establish the role of head computed tomography (CT) and magnetic resonance imaging (MRI) in adults presenting to the emergency department (ED) with first-time seizure (FS) and to analyze the potential predictor variables for the adverse imaging outcome. We retrospectively reviewed the medical records of all adults who underwent cranial CT or MRI between January 1, 2011, and December 1, 2016, to an academic ED for FS. Patients were excluded if were under 18 years of age, had known recent intracranial pathology, known brain tumor or having a history of trauma. Important predictive variables to indicate pathology in either CT or MR scan in patients with FS were evaluated with logistic regression analysis. A total of 546 FS (293 men and 253 women; range, 18-81 years; mean, 47 years) were identified in patients receiving either cranial CT or MR scan. Of them, abnormal findings were observed in 22/451 (4.8%) patients on CT and 18/95 (18.9%) patients on MRI. Predictor variables of age greater than 50 years, focal neurologic deficit, hypoglycemia, and history of malignancy were identified on CT, whereas a history of malignancy, age greater than 50 years and focal neurological deficit were determined on MRI. Limiting neuroimaging to this population would potentially reduce head CT scans by 67% and would potentially reduce head MRI scans by 47%. Clinical suspicion should be heightened and the neuroimaging should be considered for advanced age, history of malignancy, hypoglycemia or focal neurological deficits in patients with FS.

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Germline CDH1 mutation carriers are at risk for early-onset diffuse gastric cancer (DGC) and female carriers have an additional risk of lobular breast cancer. The reported literature GC risk of 70% has led to the recommendation for germline mutation carriers to undergo prophylactic total gastrectomy (PTG). The objective of this research was to examine post-surgical clinical outcomes and to identify which of the domains/symptoms from the European Organisation for Research and Treatment of Cancer QOL Questionnaire (EORTC QLQ-C30) were determinants of overall quality of life (QOL) in individuals undergoing PTG. Participants were recruited through multiple sources. Postsurgical clinical outcomes were obtained from hospital records. Participants completed validated questionnaires measuring generic and condition specific QOL (PROMIS, EORTC and SF 36v.II) at a single point in time. The mean QOL in this cohort was 70.6 (SD = 25.6), which is better than reference values from the general populations in USA and Canada Role and social function plus the symptoms anxiety, pain, taste, dyspnea and diarrhea were significant predictor variables for QOL (p < 0.05). Although this study reveals good overall QOL for individuals after PTG, attention should be given to managing symptoms as part of long term care to further enhance QOL. The function/symptom scores were associated with worse overall health and global health status and thus may mark a real need for more attentive post-surgical care.

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BACKGROUND: There is a growing nursing literature that views missed care as an inevitable consequence of work intensification associated with the rationing of nursing and material resources available to deliver care. Global studies recognize that missed care is now ubiquitous, although studies tend to be conducted in one region, rather than nationwide. This study seeks to understand the Australian context of missed care. AIMS: To explore self-reported reasons for missed care and to identify the main factors for predicting missed care within a sample of Australian nurses and midwives working in public and private hospitals in New South Wales, Victoria, Tasmania, and South Australia. METHODS: A nonexperimental, descriptive method using Kalisch's (2006) MISSCARE survey was used. Responses from 1,195 nursing and midwifery staff with differing qualifications, English language skills, and Australian employment settings were analyzed using Rasch analysis and then modeled using the Structural Equation Modeling. RESULTS: The frequency of missed care on the morning shift directly impacted on higher priority care missed during the afternoon shift. Staff skill mix imbalances and perceived inadequacy of staff numbers for the work demands further exacerbated all aspects of care during afternoon shifts. Other major factors associated with missed care were the different clinical work settings and staff to patient ratios. LINKING EVIDENCE TO ACTION: The incidences, types, and reasons behind missed care are a multidimensional construct which can be predicted when known significant factors behind missed care are simultaneously accounted for.

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BACKGROUND: Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identification of vulnerable infants at high-risk for death post-discharge is important in order to craft interventional programs. OBJECTIVES: To determine potential predictor variables for post-discharge mortality in infants less than one year of age who are likely to die after discharge from health facilities in the developing world. METHODS: A two-round modified Delphi process was conducted, wherein a panel of experts evaluated variables selected from a systematic literature review. Variables were evaluated based on (1) predictive value, (2) measurement reliability, (3) availability, and (4) applicability in low-resource settings. RESULTS: In the first round, 18 experts evaluated 37 candidate variables and suggested 26 additional variables. Twenty-seven variables derived from those suggested in the first round were evaluated by 17 experts during the second round. A final total of 55 candidate variables were retained. CONCLUSION: A systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting.

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Purpose The purpose of this study is to measure the psychometric properties of the Work and Wellbeing Inventory (WBI) (in Dutch: VAR-2), a screening tool that is used within occupational health care and rehabilitation. Our research question focused on the reliability and validity of this inventory. Methods Over the years seven different samples of workers, patients and sick listed workers varying in size between 89 and 912 participants (total: 2514), were used to measure the test-retest reliability, the internal consistency, the construct and concurrent validity, and the criterion and predictive validity. Results The 13 scales displayed good internal consistency and test-retest reliability. The constructive validity of the WBI could clearly be demonstrated in both patients and healthy workers. Confirmative factor analyses revealed a CFI >.90 for all scales. The depression scale predicted future work absenteeism (>6 weeks) because of a common mental disorder in healthy workers. The job strain scale and the illness behavior scale predicted long term absenteeism (>3 months) in workers with short-term absenteeism. The illness behavior scale moderately predicted return to work in rehab patients attending an intensive multidisciplinary program. Conclusions The WBI is a valid and reliable tool for occupational health practitioners to screen for risk factors for prolonged or future sickness absence. With this tool they will have reliable indications for further advice and interventions to restore the work ability.

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Resumen: Entender y predecir variaciones en el tamaño de los invertebrados megabentónicos sigue siendo un desafío importante en macroecología marina. Este estudio se realizó para identificar tendencias de cambio en la talla de la megafauna bentónica de fondos sedimentarios tropicales y conocer la influencia de variables que pueden determinar el tamaño de estos organismos, evaluando hipótesis y paradigmas ecológicos de mar profundo originados en zonas subtropicales y templadas. El área de estudio abarcó toda la plataforma continental del Caribe colombiano. Los ejemplares se recolectaron en 1998, 2001 y 2005 con red demersal semi-globo, entre 10 y 500 m de profundidad. Se seleccionaron las especies mejor representadas: Eudolium crosseanum, Cosmioconcha nitens, Nuculana acuta (moluscos), Astropecten alligator, Brissopsis atlantica, B. elongata (equinodermos), Anasimus latus, Chasmocarcinus cylindricus y Achelous spinicarpus (crustáceos). Para detectar tendencias significativas de cambio de tamaño, e inferir la influencia de variables bióticas y ambientales, se emplearon modelos aditivos generalizados, donde la talla fue la variable respuesta y las variables predictivas fueron: profundidad, temperatura, densidad intraespecífica e interespecífica, riqueza, latitud y longitud. Se midieron 7 000 ejemplares en total. Seis especies presentaron un incremento de la talla con el aumento de la profundidad y la disminución de la temperatura. Dichas especies abarcaron ámbitos batimétricos que superaron los 10 °C de diferencia entre zonas someras y profundas. Geográficamente se presentó una marcada disminución de tallas en el área de influencia de la desembocadura del río Magdalena, debida posiblemente a los fuertes cambios físicosquímicos que este río genera, principalmente porque es la zona con menos productividad primaria planctónica de toda el área de estudio. Hacia el norte de la desembocadura del río Magdalena se presentó un notorio aumento de tamaños entre los (74° - 71°W y 11° - 13°N), que puede deberse a la surgencia que se presenta en el norte del Caribe colombiano. La relación entre la densidad de individuos y la talla no fue clara. Sin embargo cinco especies mostraron una relación inversa con la densidad intraespecífica, y tres con la interespecífica. Temperatura y profundidad fueron las variables que mejor explicaron los cambios de talla detectados. La mayoría de especies presentaron un aumento de dimensiones corporales con la disminución de la temperatura en el perfil batimétrico. La tendencia al aumento de tallas con la profundidad es contraria a lo indicado por los modelos teóricos de talla óptima (pero acorde con algunos estudios más recientes), que predicen un decrecimiento del tamaño de los organismos con el incremento de la profundidad, debido a la disminución de nutrientes hacia zonas profundas del océano. Es posible que el aumento de tamaño sea una estrategia adaptativa para la maximización de la energía, como ocurre con muchos organismos en las porciones más frías de su ámbito de distribución. Futuros estudios en el Caribe deberían examinar variaciones en la talla de la megafauna bentónica en zonas más profundas (más de 500 m), donde la temperatura varía menos, razón por la cual otros factores pueden ser más importantes para determinar el tamaño de estos organismos.

Abstract: Understanding and predicting adaptations in body size of megabenthic invertebrates remains a major challenge in marine macroecology. This study was conducted in order to investigate size variations of benthic megafauna in the tropics and to identify the effect of biotic and abiotic factors that may produce changes to these organisms, testing unresolved hypothesis and paradigms of deep sea ecology from subtropical and temperate areas. The study area covered the continental shelf of the Colombian Caribbean. The samples were collected during 1998, 2001 and 2005, using semi-globe demersal net for a water depth of 10 to 500 m. The most common species were selected for further study: Eudolium crosseanum, Cosmioconcha nitens, Nuculana acuta (mollusks), Astropecten alligator, Brissopsis atlantica, B. elongata (equinoderms), Anasimus latus, Chasmocarcinus cylindricus and Achelous spinicarpus (crustaceans). Generalized Additive Models were used to detect significant changes in size and to infer the effects of biotic and environmental factors on organisms' size. The dependent variable was size and the predicting model variables were depth, temperature, intraspecific density, interspecific density, richness, latitude, and longitude. A total of 7 000 individuals were measured. Six species showed an increase in body size towards deeper and colder sites. These species inhabit shallow and deep environments that exceed a variation in temperature of 10 °C. There was a remarkable size reduction in areas affected by the Magdalena River, possibly due to major physicochemical changes caused by the river. This region has the lowest planktonic primary productivity within the study area. An increase in sizes was observed north of the Magdalena River (74° - 71°W & 11° - 13°N), which may be attributable to the coastal upwelling occurring in this part of Colombia. The relationship between the density of benthic organisms and size was not clear. However, five species showed an inverse relation with intraspecific density and three with interspecific density. Temperature and depth were the variables that best explained the variations in size. Most of the studied species showed an increase in body size when temperature dropped along the bathymetric range. The trend of increasing size in deeper zones is contrary to the prediction of the optimal size theoretical model (but consistent with recent studies), which indicates a reduction in organisms' size in the deep sea, due to food limitation with increasing depth. It is possible that this increase in size is an adaptation to maximize energy, which is frequently observed in the coldest habitats of several species. Future studies in Caribbean should examine variations in size of benthic megafauna towards deeper zones (more than 500 m), were temperature is less variable and then other factors can play a more important role determining the size of these organisms. Rev. Biol. Trop. 65 (3): 1002-1021. Epub 2017 September 01.

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The study had two goals: (1) to explore the relations between self-assessed childhood depression and other adaptive and clinical variables (2) to identify predictor variables of childhood depression. Participants were 420 students aged 7-10 years old (53.3% boys, 46.7% girls). Results revealed: (1) positive correlations between depression and clinical maladjustment, school maladjustment, emotional symptoms, internalizing and externalizing problems, problem behaviors, emotional reactivity, and childhood stress; and (2) negative correlations between depression and personal adaptation, global self-concept, social skills, and resilience (sense of competence and affiliation). Linear regression analysis including the global dimensions revealed 4 predictors of childhood depression that explained 50.6% of the variance: high clinical maladjustment, low global self-concept, high level of stress, and poor social skills. However, upon introducing the sub-dimensions, 9 predictor variables emerged that explained 56.4% of the variance: many internalizing problems, low family self-concept, high anxiety, low responsibility, low personal self-assessment, high social stress, few aggressive behaviors toward peers, many health/psychosomatic problems, and external locus of control. The discussion addresses the importance of implementing prevention programs for childhood depression at early ages.

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Relatively few studies have addressed predictors of first-attempt outcomes (pass-fail) on the National Council Licensure Examination-Registered Nurses (NCLEX-RN) for accelerated BSN programs. The purpose of this study was to compare potential predictors of NCLEX outcomes in graduates of first-degree accelerated (FDA; n=62) and second-degree accelerated (SDA; n=173) BSN programs sharing a common nursing curriculum. In this retrospective study, bivariate analyses and multiple logistic regression assessed significance of selected demographic and academic characteristics as predictors of NCLEX-RN outcomes. FDA graduates were more likely than SDA graduates to fail the NCLEX-RN (P=.0013). FDA graduates were more likely to speak English as a second or additional language (P<.0001), have lower end-of-program GPA and HESI Exit Exam scores (both P<.0001), and have a higher proportions of grades ≤ C (P=.0023). All four variables were significant predictors of NCLEX-RN outcomes within both FDA and SDA programs. The only significant predictors in adjusted logistic regression of NCLEX-RN outcome for the pooled FDA+SDA graduate sample were proportion of grades ≤ C (a predictor of NCLEX-RN failure) and HESI Exit Exam score (a predictor of passing NCLEX-RN). Grades of C or lower on any course may indicate inadequate mastery of critical NCLEX-RN content and increased risk of NCLEX-RN failure.

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INTRODUCTION AND HYPOTHESIS: To establish the contribution of maternal, fetal and intrapartum factors to the risk of incidence of obstetric anal sphincter injuries (OASIS) and assess the feasibility of an OASIS risk prediction model based on variables available to clinicians prior to birth. METHODS: This was a population-based, retrospective cohort study using single-site data from the birth database of Aarhus University Hospital, Denmark. The participants were all women who had a singleton vaginal birth during the period 1989 to 2006. Univariate and multivariate logistic regression analyses were performed using multiple imputations for missing data and internally validated using bootstrap methods. The main outcome measures were the contributions of maternal, fetal and intrapartum events to the incidence of OASIS. RESULTS: A total of 71,469 women met the inclusion criteria, of whom 1,754 (2.45 %) sustained OASIS. In the multivariate analysis of variables known prior to birth, maternal age 20 - 30 years (OR 1.65, 95 % CI 1.44 - 1.89) and ≥30 years (OR 1.60, 95 % CI 1.39 - 1.85), occipitoposterior fetal position (OR 1.34, 95 % CI 1.06 - 1.70), induction/augmentation of labour (OR 1.46, 95 % CI 1.32 - 1.62), and suspected macrosomia (OR 2.20, 95 % CI 1.97 - 2.45) were independent significant predictors of OASIS, with increasing parity conferring a significant protective effect. The 'prebirth variable' model showed a 95 % sensitivity and a 24 % specificity in predicting OASIS with 1 % probability, and a 3 % sensitivity and a 99 % specificity in predicting OASIS with a 10 % probability. CONCLUSIONS: Our model identified several significant OASIS risk factors that are known prior to actual birth. The prognostic model shows potential for ruling out OASIS (high sensitivity with a low risk cut-off value), but is not useful for ruling in the event.

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BACKGROUND: Post-discharge mortality is a frequent but poorly recognized contributor to child mortality in resource limited countries. The identification of children at high risk for post-discharge mortality is a critically important first step in addressing this problem. OBJECTIVES: The objective of this project was to determine the variables most likely to be associated with post-discharge mortality which are to be included in a prediction modelling study. METHODS: A two-round modified Delphi process was completed for the review of a priori selected variables and selection of new variables. Variables were evaluated on relevance according to (1) prediction (2) availability (3) cost and (4) time required for measurement. Participants included experts in a variety of relevant fields. RESULTS: During the first round of the modified Delphi process, 23 experts evaluated 17 variables. Forty further variables were suggested and were reviewed during the second round by 12 experts. During the second round 16 additional variables were evaluated. Thirty unique variables were compiled for use in the prediction modelling study. CONCLUSION: A systematic approach was utilized to generate an optimal list of candidate predictor variables for the incorporation into a study on prediction of pediatric post-discharge mortality in a resource poor setting.

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Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article.