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Resumen Introducción: la infección por COVID 19 corresponde actualmente al evento infeccioso con mayor impacto en salud púbica a nivel mundial, en Colombia, al 30 de abril de 2020 se registraron 6465 casos acumulados, 360 defunciones y 2186 casos recuperados, dado el aumento en los casos reportados mediante los sistemas de vigilancia epidemiológica se precisa de herramientas que faciliten el diagnóstico oportuno y la predicción en el comportamiento de los casos a nivel nacional. Objetivos: proponer un modelo estadístico que permita predecir la probabilidad de cursar con diagnóstico de COVID-19 en la población atendida por sospecha de infección por el mismo en una institución de tercer nivel del municipio de Pereira- Risaralda entre marzo y abril de 2020. Materiales y métodos: se presenta un estudio descriptivo de corte trasversal en el cual se analizaron 82 casos, se realizó un modelo predictivo basado en compuertas lógicas AND y OR, y análisis por estadística descriptiva e inferencial. Resultados: de los 82 registros analizados se encontró una relación hombre: mujer de 1:2; el 6% de los pacientes tuvo alta probabilidad para diagnóstico de COVID 19, el 20% tuvo probabilidad intermedia y el 72% registró baja probabilidad para COVID19, la concordancia del modelo con los resultados de las pruebas fue inferior a 0,5. Conclusiones: el modelo estadístico planteado fue insuficiente para lograr la predicción de la totalidad de los casos de COVID-19 basados en el perfil de riego de la población, se precisan nuevas investigaciones con tamaños de muestra superiores, diseños y análisis distintos. MÉD.UIS.2022;35(1): 57-69.

Abstract Introduction: COVID 19 infection currently corresponds to the infectious event with the greatest impact on public health worldwide, in Colombia, as of April 30, 2020, 6465 accumulated cases, 360 deaths and 2186 recovered cases were registered, given the increase in cases reported through epidemiological surveillance systems, tools are needed to facilitate timely diagnosis and prediction in the behavior of cases at the national level. Objectives: to propose a statistical model that allows predicting the probability of a diagnosis of COVID-19 in the population treated for suspected coronavirus infection in a third-level institution in the population of Pereira-Risaralda between March and April 2020. Materials and methods: a descriptive cross-sectional study is presented, in which 82 cases were analyzed, a predictive model based on AND and OR logic gates, analyzes by descriptive and inferential statistics were performed. Results: of the 82 records analyzed, a male: female ratio of 1: 2 was found; 6% of the patients had a high probability for the diagnosis of COVID 19, 20% had an intermediate probability and 72% had a low probability for COVID19, the agreement of the model with the test results was less than 0.5. Conclusions: the proposed statistical model was insufficient to achieve the prediction of all the cases of COVID-19 based on the irrigation profile of the population. New investigations are required with larger sample sizes associated with longitudinal designs and combined statistical analyzes that allow to refine the proposed model. MÉD.UIS.2022;35(1): 57-69.

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BACKGROUND: There is considerable overlap between pain referral patterns from the lumbar disc, lumbar facets, the sacroiliac joint (SIJ), and the hip. Additionally, sciatic like symptoms may originate from the lumbar spine or secondary to extra-spinal sources such as deep gluteal syndrome (GPS). Given that there are several overlapping potential anatomic sources of symptoms that may be synchronous in patients who have low back pain (LBP), it may not be realistic that a linear deductive approach can be used to establish a diagnosis and direct treatment in this group of patients. OBJECTIVE: The objective of this theoretical clinical reasoning model is to provide a framework to help clinicians integrate linear and non-linear clinical reasoning approaches to minimize clinical reasoning errors related to logically fallacious thinking and cognitive biases. METHODS: This masterclass proposes a hypothesis-driven and probabilistic approach that uses clinical reasoning for managing LBP that seeks to eliminate the challenges related to using any single diagnostic paradigm. CONCLUSIONS: This model integrates the why (mechanism of primary symptoms), where (location of the primary driver of symptoms), and how (impact of mechanical input and how it may or may not modulate the patient's primary complaint). The integration of these components individually, in serial, or simultaneously may help to develop clinical reasoning through reflection on and in action. A better understanding of what these concepts are and how they are related through the proposed model may help to improve the clinical conversation, academic application of clinical reasoning, and clinical outcomes.

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Introduction: The analysis of injuries caused by traffic from a physical and mathematical perspective can help improve road safety strategies. Objective: Predict the dynamics of traffic fatalities in the states of Maryland and Massachusetts for the years 2004 and 2014 in the context of probabilistic random walk. Methods: An analysis was made of the number of total fatalities caused by traffic per year, in the states of Maryland and Massachusetts between the years 1994-2003 and 1994-2013. The behavior of these values was analyzed as a probabilistic random walk; for this, the probabilistic lengths were found for each year, during the period studied and four probability spaces were analyzed, with which it was possible to analyze their behavior, to establish a prediction of the number of total fatalities caused by traffic for the years 2004 and 2014. Results: The predictions for the years 2014 and 2004 for Maryland and Massachusetts when compared with the real values, the percentage of success was 98%. Main conclusion: The predictions for the years 2014 and 2004 for Maryland and Massachusetts when compared with the real values, the percentage of success was 98%. Conclusions: the behavior of traffic fatalities in Maryland and Massachusetts presented a predictable self-organization from the context of probabilistic random walk, constituting a useful tool for analyzing the operation of road safety strategies.

Antecedentes: El análisis de los accidentes de tránsito desde una perspectiva física y matemática puede ayudar a mejorar las estrategias viales de seguridad. Objetivo: Obtener una predicción de la dinámica de fatalidades a causa del tráfico en los estados de Maryland y Massachusetts para los años 2004 y 2014 en el contexto de la caminata al azar probabilista. Métodos: Se realizó un análisis del número de fatalidades totales causadas por el tráfico al año, en los estados de Maryland y Massachusetts entre los años 1994-2003 y 1994-2013. El comportamiento de estos valores fue analizado como una caminata al azar probabilista; para ello se hallaron las longitudes probabilistas para cada año, durante el periodo estudiado y se analizaron cuatro espacios de probabilidad, con los que fue posible analizar su comportamiento, para establecer una predicción del número de fatalidades totales causadas por el tráfico para los años 2004 y 2014. Resultados: Las predicciones para los años 2014 y 2004 para Maryland y Massachusetts al ser comparados con los valores reales el porcentaje de acierto fue del 98%. Conclusión principal: el comportamiento de las fatalidades de tráfico en Maryland y Massachusetts presentó una autoorganización predecible desde el contexto de la caminata al azar probabilista, constituyéndose como una herramienta útil para el análisis del funcionamiento de las estrategias de seguridad vial.

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Resumen Introducción: Previamente se desarrolló una nueva metodología de ayuda diagnóstica para los registros Holter fundamentada en los sistemas dinámicos y la teoría de probabilidad, a partir de la información registrada en 21 horas. Objetivo: Evaluar la capacidad diagnóstica de esta metodología durante 19 horas, comparándola con los resultados convencionales del Holter y con los resultados del método matemático aplicado en 21 horas. Materiales y Métodos: fueron evaluados 80 casos de pacientes mayores a 20 años, 10 con registro Holter normal y 70 diagnosticados de forma convencional con diferentes patologías cardíacas. Se establecieron los rangos para las frecuencias cardíacas y de número de latidos por hora en 21 y 19 horas; luego, se calculó la probabilidad de ocurrencia de estos, lo que permitió diferenciar estados de normalidad y enfermedad aguda a partir de tres parámetros. Se comparó el diagnóstico físico-matemático con el diagnóstico convencional, tomado como Gold Standard. Resultados: De los casos normales, dos presentaron probabilidad menor o igual a 0,217 y ocho probabilidades mayores o igual a 0,304; ningún caso de enfermedad aguda presentó valores con probabilidad menor o igual a 0,217, mientras que todos presentaron valores mayores o iguales a 0,304, tanto para los registros Holter evaluados en 21 como en 19 horas. Conclusiones: Se confirmó la utilidad clínica de la metodología ante una reducción del tiempo de evaluación a 19 horas, obteniendo diagnósticos objetivos con base en la auto-organización matemática del fenómeno.

Abstract Background: a new method to help evaluate 21-hour holter recordings based on dynamic systems and the theory of probability was previously developed Aim: to evaluate the diagnostic value of this methodology in the analysis of 19 hr compared to conventional holter analysis over a 21-hr recording. Methods: the holter recordings of 80 subjects aged over 20 years old were analyzed. Ten subjects had a normal holter and 70 conventionally diagnosed as abnormal. Ranges for heart rate and number of beats in 21 or 19 hours were determined. The probability of their occurrence was calculated using 3 parameters. The mathematically derived diagnosis was compared to the clinical diagnosis, considered a gold standard. Results: Among normal cases the calculated probability was ≤ 0.217 in 2 cases and ≥0.304 in 8. No case with acute disease presented probability values ≤0.217; all had probability values ≥0.304, both in 21 and 19 hour recordings. Conclusion: the mathematical methodology described was clinically useful allowing a reduction in recording time from 21 to 19 hr. Clinical diagnosis may be inferred from the mathematical organization of a holter recording.

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Autism spectrum disorders (ASDs) are characterized by deficits in the individual’s ability to socialize, communicate, and use the imagination, in addition to stereotyped behaviors. These disorders have a heterogenous phenotype, both in relation to symptoms and regarding severity. Organic problems related to the gastrointestinal tract are often associated with ASD, including dysbiosis, inflammatory bowel disease, exocrine pancreatic insufficiency, celiac disease, indigestion, malabsorption, food intolerance, and food allergies, leading to vitamin deficiencies and malnutrition. In an attempt to explain the pathophysiology involved in autism, a theory founded on opioid excess has been the focus of various investigations, since it partially explains the symptomatology of the disorder. Another hypothesis has been put forward whereby the probable triggers of ASDs would be related to the presence of bacteria in the bowel, oxidative stress, and intestinal permeability. The present update reviews these hypotheses.

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Las diversas acepciones del concepto del riesgo, consideradas en el marco de condicionantes ambientales, genéticas y culturales presentan retos a los investigadores que pretenden aplicar la estimación de un evento como parte de los objetivos investigativos. A través de este ensayo se realizará una breve disertación sobre la noción de riesgo y probabilidad, incluyendo conceptos de estimación referenciados dentro de aspectos lógicos, científicos, investigativos, culturales y genéticos que permitan aplicar el concepto de incertidumbre y predicción a la comprensión de varios tipos de fenómenos, tales como los fenotipos complejos, fenotipos físicos o comportamientos culturales. Una vez se establezcan las bases conceptuales de riesgo y probabilidad, se integrarán elementos de herencia y ambiente que permiten desarrollar el concepto más allá de la noción numérica, de tal modo que los conceptos estadísticos junto con las connotaciones culturales del concepto de riesgo, develen la utilidad de la comprensión probabilística en relación al desarrollo evolutivo de la complejidad fenotípica.

The various meanings of the concept of risk, considered in the context of environmental, genetic and cultural conditions, present diverse challenges to researchers who seek to apply event estimation processes as a part of their research objectives. Through this essay, I’ll develop a brief exposition about the notion of risk and probability, including estimation concepts referenced within logical aspects, scientific methods, research techniques and cultural and genetic aspects; concepts necessary to implement the notion of prediction and uncertainty in the understanding of various types of phenomena, such as complex phenotypes and cultural or behavioral phenotypes. Once the conceptual basis of risk and probability is established, I will integrate heredity and environment elements allowing the development of the concept beyond the numerical notion, therefore the statistical concepts along with the cultural connotations of the concept of risk will unveil the usefulness of probabilistic understanding in relation to the evolutionary development of phenotypic complexity.

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