Assisting the Learning of Clinical Reasoning by Veterinary Medical Learners with a Case Example.

Effective clinical reasoning is essential for veterinary medical education, particularly in managing complex cases. This review explores strategies for learning clinical reasoning by veterinary medical learners, using a case example of mastitis to illustrate key concepts. Clinical reasoning encompasses cognitive, metacognitive, social, and situational activities, yet the literature on practical applications in veterinary education remains limited. The review discusses various stages of clinical reasoning, including data collection, problem representation, differential diagnosis, and management planning. It emphasizes the importance of integrating client-centered care and iterative evaluation into the clinical decision-making process. Key learning strategies include facilitation in using the domains of clinical reasoning-concepts, data collection, and analysis, taking action, and reflection on encounters. This review highlights best practices such as forward and backward reasoning, reflective practice, and the use of practical examples to enhance learners' diagnostic accuracy and patient outcomes. The insights provided aim to enhance the training of veterinary learners, ensuring they can navigate day 1 as well as complex cases with improved diagnostic accuracy and patient outcomes.

Conceptualización teórica y metodológica del análisis situacional: un enfoque de la teoría fundamentada / Theoretical and methodological con-ceptualization of situational analysis: A grounded theory approach

La Teoría Fundamentada ha sido un método de investigación de gran importancia para los estudios cualitativos. La corriente posmoderna, posestructuralista e interpretativa del análisis situacional propuesto por Adele Clarke ofrece una nueva visión amplia, compleja y heterogénea de los fenómenos sociales de la naturaleza, representados en tres enfoques cartográficos de análisis: (a) mapa de situación, (b) mapa de mundos/arenas sociales y (c) mapa de posiciones. En este escrito se esbozan las conceptualizaciones teóricas del análisis situacional, sus nuevas bases epistemológicas y la ruta de construcción de cada uno de los mapas situacionales. Dicho método ofrece una herramienta innovadora para estudiar fenómenos desde una visión crítico-social que fomenta el cuidado de enfermería con posturas diferenciadoras en el quehacer.(AU)

Grounded Theory has been a research method of great importance for qualitative studies. The postmodern, poststructuralist and interpretive current of situational analysis (SA) proposed by Adele Clarke offers a new broad, complex and heterogeneous vision of the social phenomena of nature, represented in three cartographic approaches of analysis: (a) situation map, (b) map of worlds/social arenas and (c) map of positions. In this paper, the theoretical conceptualizations of situational analysis, its new epistemological bases and the construction route of each of the situational maps are briefly outlined. This method offers an innovative tool to study phenomena from a critical-social vision that promotes nursing care with differentiating positions in the work.(AU)

An extended version of Necessary Condition Analysis (NCA) allows more specific conclusions: an example involving well-being and resilience.

BACKGROUND: After conducting necessary condition analysis (NCA), researchers have concluded that a certain, not too low, level of well-being is necessary but not sufficient for a high level of resilience. However, as acknowledged by the developers of the test, NCA only evaluates if the association between two variables is characterized by some unspecified type of non-randomness and not conditions of necessity. METHOD: Earlier reported data on the association between well-being and resilience among Filipino adults (N = 533) in COVID-19 quarantine were re-analyzed with an extended version of NCA. RESULTS: Analyses indicated a significant necessity effect of resilience on overall well-being, which is not logically compatible with well-being being necessary but not sufficient for resilience. Analyses with an extended version of NCA suggested that the association between overall well-being and resilience was characterized by equal degrees of necessity and sufficiency. CONCLUSIONS: The original version of NCA is only capable of evaluating if the association between two variables is characterized by some unspecified type of non-randomness. The extended version of NCA allows researchers to draw more specific conclusions.

Post-Analysis of Predictive Modeling with an Epidemiological Example.

Post-analysis of predictive models fosters their application in practice, as domain experts want to understand the logic behind them. In epidemiology, methods explaining sophisticated models facilitate the usage of up-to-date tools, especially in the high-dimensional predictor space. Investigating how model performance varies for subjects with different conditions is one of the important parts of post-analysis. This paper presents a model-independent approach for post-analysis, aiming to reveal those subjects' conditions that lead to low or high model performance, compared to the average level on the whole sample. Conditions of interest are presented in the form of rules generated by a multi-objective evolutionary algorithm (MOGA). In this study, Lasso logistic regression (LLR) was trained to predict cardiovascular death by 2016 using the data from the 1984-1989 examination within the Kuopio Ischemic Heart Disease Risk Factor Study (KIHD), which contained 2682 subjects and 950 preselected predictors. After 50 independent runs of five-fold cross-validation, the model performance collected for each subject was used to generate rules describing "easy" and "difficult" cases. LLR with 61 selected predictors, on average, achieved 72.53% accuracy on the whole sample. However, during post-analysis, three categories of subjects were discovered: "Easy" cases with an LLR accuracy of 95.84%, "difficult" cases with an LLR accuracy of 48.11%, and the remaining cases with an LLR accuracy of 71.00%. Moreover, the rule analysis showed that medication was one of the main confusing factors that led to lower model performance. The proposed approach provides insightful information about subjects' conditions that complicate predictive modeling.

A simple model for the total number of SARS-CoV-2 infections on a national level.

This study aimed to identify an appropriate simple mathematical model to fit the number of coronavirus disease 2019 (COVID-19) cases at the national level for the early portion of the pandemic, before significant public health interventions could be enacted. The total number of cases for the COVID-19 epidemic over time in 28 countries was analysed and fit to several simple rate models. The resulting model parameters were used to extrapolate projections for more recent data. While the Gompertz growth model (mean R2 = 0.998) best fit the current data, uncertainties in the eventual case limit introduced significant model errors. However, the quadratic rate model (mean R2 = 0.992) fit the current data best for 25 (89%) countries as determined by R2 values of the remaining models. Projection to the future using the simple quadratic model accurately forecast the number of future total number of cases 50% of the time up to 10 days in advance. Extrapolation to the future with the simple exponential model significantly overpredicted the total number of future cases. These results demonstrate that accurate future predictions of the case load in a given country can be made using this very simple model.

Analítica de datos: incidencia de la contaminación ambiental en la salud pública en Medellín (Colombia) / Data Analytics: incidence of air pollution on public health in Medellin, Colombia

RESUMEN Objetivo Analizar el impacto de la contaminación del aire por material particulado PM2,5 y su relación con el número de asistencias a entidades de salud por enfermedades respiratorias por medio de analítica de datos. Métodos Se analizaron datos del Área Metropolitana de Medellín, Colombia, ciudad ubicada en un valle estrecho densamente poblado e industrializado y que ha presentado episodios críticos de contaminación en los últimos años. Se analizaron tres fuentes de datos: datos meteorológicos aportados por el SIATA (Sistema de Alerta Temprana de Medellín y el Valle de Aburrá); datos de contaminación por material particulado PM2,5 aportados por SIATA; y reportes de los RIPS (Registros Individuales de Prestación de Servicios de Salud) aportados por la Secretaría de Salud. Resultados Se evidenció la relación entre la concentración de PM2,5 con las asistencias médicas por los diagnósticos de IRA, EPOC y asma. En un episodio crítico de contaminación por PM2,5, se encontraron los siguientes retardos en la atención médica: entre 0 y 2 días para el IRA, 0 y 7 días para el EPOC y 0 y 5 días para el asma. Discusión Se encontraron coeficientes de correlación que evidencian la asociación de la concentración de PM2,5 con las asistencias por los diagnósticos de IRA, EPOC y asma. La mayor correlación entre las tres morbilidades se presentó para el asma. La variable meteorológica de mayor correlación con la variable objetivo es la temperatura del aire para el caso de EPOC y asma. En el caso de IRA, la variable con mayor correlación es la velocidad del viento. Por otro lado, el día de la semana es una variable de gran importancia a la hora de realizar un estudio de atenciones por enfermedades.

ABSTRACT Objective To analyze the impact of air pollution by PM2,5 particulate matter and its relationship with the number of attendances to health entities for respiratory diseases through data analytics. Methods Data from the Metropolitan Area of Medellín, Colombia, a city located in a densely populated and industrialized narrow valley and that has presented critical episodes of contamination in recent years, were analyzed. Three data sources were analyzed: meteorological data provided by SIATA (Early Warning System of Medellín and the Aburra Valley), PM2,5 particulate matter contamination data provided by SIATA, and RIPS reports (Individual Registers for the Provision of Health Services) provided by the health department. Results The relationship between the concentration of PM2,5 and medical care for the diagnoses of ARI, COPD and asthma was evidenced. In a critical episode of PM2,5 contamination, the following delays in medical care were found: between 0-2 days for IRA, 0-7 days for COPD, and 0-5 days for asthma. Discussion Correlation coefficients were found that show the association of the concentration of PM2,5 with the attendances for the diagnoses of ARI, COPD, and asthma. The highest correlation between the three morbidities was found for asthma. The meteorological variable with the highest correlation with the objective variable is air temperature in the case of COPD and asthma. In the case of IRA, the variable with the highest correlation is wind speed. On the other hand, the day of the week is a variable of great importance when carrying out a study of care for diseases.

Social network analysis of COVID-19 transmission in Karnataka, India.

We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11-40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1-47 for 199 (17.35%) nodes, and betweenness, 0.5-87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) 'super-spreaders' (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

Benford´s Law with small sample sizes: A new exact test useful in health sciences during epidemics / Ley de Benford con muestras pequeñas: una prueba exacta nueva útil ciencias de la salud durante epidemias

Abstract Benford or "first digit" law has been used successfully to evaluate epidemiological surveillance systems, especially during epidemics. Conventional statistical methods for evaluation (x2 and log-likelihood ratio) are controversial when the number of data is small (n <7). In this methodological note a new test is proposed to evaluate compliance with Benford's law with small samples, which can be used with biomedical, medical and public health data.

Resumen La ley de Benford o de los "primeros dígitos" ha sido usada exitosamente para evaluar los sistemas de vigilancia epidemiológica, en especial durante epidemias. Los métodos estadísticos convencionales para la evaluación (x 2 y razón de log-verosimilitud) son controversiales cuando los datos son poco (n<7). En esta nota metodológica se propone una nueva prueba para evaluar el cumplimiento de la ley de Benford con muestras pequeñas, que puede ser usada con datos de biomedicina, medicina y salud pública.

Analysis of individual patient data to describe the incubation period distribution of Shiga-toxin producing Escherichia coli.

Shiga-toxin producing Escherichia coli (STEC) is a pathogen that can cause bloody diarrhoea and severe complications. Cases occur sporadically but outbreaks are also common. Understanding the incubation period distribution and factors influencing it will help in the investigation of exposures and consequent disease control. We extracted individual patient data for STEC cases associated with outbreaks with a known source of exposure in England and Wales. The incubation period was derived and cases were described according to patient and outbreak characteristics. We tested for heterogeneity in reported incubation period between outbreaks and described the pattern of heterogeneity. We employed a multi-level regression model to examine the relationship between patient characteristics such as age, gender and reported symptoms; and outbreak characteristics such as mode of transmission with the incubation period. A total of 205 cases from 41 outbreaks were included in the study, of which 64 cases (31%) were from a single outbreak. The median incubation period was 4 days. Cases reporting bloody diarrhoea reported shorter incubation periods compared with cases without bloody diarrhoea, and likewise, cases aged between 40 and 59 years reported shorter incubation period compared with other age groups. It is recommended that public health officials consider the characteristics of cases involved in an outbreak in order to inform the outbreak investigation and the period of exposure to be investigated.

Estimating within-flock transmission rate parameter for H5N2 highly pathogenic avian influenza virus in Minnesota turkey flocks during the 2015 epizootic.

Better control of highly pathogenic avian influenza (HPAI) outbreaks requires deeper understanding of within-flock virus transmission dynamics. For such fatal diseases, daily mortality provides a proxy for disease incidence. We used the daily mortality data collected during the 2015 H5N2 HPAI outbreak in Minnesota turkey flocks to estimate the within-flock transmission rate parameter (ß). The number of birds in Susceptible, Exposed, Infectious and Recovered compartments was inferred from the data and used in a generalised linear mixed model (GLMM) to estimate the parameters. Novel here was the correction of these data for normal mortality before use in the fitting process. We also used mortality threshold to determine HPAI-like mortality to improve the accuracy of estimates from the back-calculation approach. The estimated ß was 3.2 (95% confidence interval (CI) 2.3-4.3) per day with a basic reproduction number of 12.8 (95% CI 9.2-17.2). Although flock-level estimates varied, the overall estimate was comparable to those from other studies. Sensitivity analyses demonstrated that the estimated ß was highly sensitive to the bird-level latent period, emphasizing the need for its precise estimation. In all, for fatal poultry diseases, the back-calculation approach provides a computationally efficient means to obtain reasonable transmission parameter estimates from mortality data.

The Ebola virus disease outbreak in Tonkolili district, Sierra Leone: a retrospective analysis of the Viral Haemorrhagic Fever surveillance system, July 2014-June 2015.

In Sierra Leone, the Ebola virus disease (EVD) outbreak occurred with substantial differences between districts with someone even not affected. To monitor the epidemic, a community event-based surveillance system was set up, collecting data into the Viral Haemorrhagic Fever (VHF) database. We analysed the VHF database of Tonkolili district to describe the epidemiology of the EVD outbreak during July 2014-June 2015 (data availability). Multivariable analysis was used to identify risk factors for EVD, fatal EVD and barriers to healthcare access, by comparing EVD-positive vs. EVD-negative cases. Key-performance indicators for EVD response were also measured. Overall, 454 EVD-positive cases were reported. At multivariable analysis, the odds of EVD was higher among those reporting contacts with an EVD-positive/suspected case (odds ratio (OR) 2.47; 95% confidence interval (CI) 2.44-2.50; P < 0.01) and those attending funeral (OR 1.02; 95% CI 1.01-1.04; P < 0.01). EVD cases from Kunike chiefdom had a lower odds of death (OR 0.22; 95% CI 0.08-0.44; P < 0.01) and were also more likely to be hospitalised (OR 2.34; 95% CI 1.23-4.57; P < 0.05). Only 25.1% of alerts were generated within 1 day from symptom onset. EVD preparedness and response plans for Tonkolili should include social-mobilisation activities targeting Ebola/knowledge-attitudes-practice during funeral attendance, to avoid contact with suspected cases and to increase awareness on EVD symptoms, in order to reduce delays between symptom onset to alert generation and consequently improve the outbreak-response promptness.

Use of whole genome sequencing to complement characterisation of a typhoid fever outbreak among a Marshallese community: Oklahoma, 2015.

Typhoid fever is an illness caused by Salmonella enterica serotype Typhi. In developing regions, it affects an estimated 20 million people annually, causing 200 000 deaths. Although uncommon, cases occur in the USA each year, predominantly due to international travel. During February 2015, the Oklahoma State Department of Health (OSDH) detected an outbreak of typhoid fever among residents of northwestern Oklahoma. OSDH conducted case-patient interviews to identify the source and symptomatic contacts. Whole genome sequencing (WGS) was performed to characterise the genetic relatedness of isolates among the four outbreak-associated pulsed-field gel electrophoresis (PFGE) patterns. We identified 38 cases, 25 confirmed and 13 probable, in two states. WGS revealed a 0-10 single-nucleotide polymorphism variation between isolates. Although we were unable to determine the source, almost all case-patients were members of the Marshallese community that attended a common event in Oklahoma, or were contacts to a confirmed case. This is the largest outbreak of typhoid fever in the USA since 1989, and first to apply WGS to complement interpretation of PFGE results during a typhoid fever outbreak investigation. This investigation illustrates the potential risk of outbreaks among communities comprised of international populations from regions where typhoid fever remains endemic.

Imported case of measles in a university setting leading to an outbreak of measles in Edinburgh, Scotland from September to December 2016.

In September 2016, an imported case of measles in Edinburgh in a university student resulted in a further 17 confirmed cases during October and November 2016. All cases were genotype D8 and were associated with a virus strain most commonly seen in South East Asia. Twelve of the 18 cases were staff or students at a university in Edinburgh and 17 cases had incomplete or unknown measles mumps rubella (MMR) vaccination status. The public health response included mass follow-up of all identified contacts, widespread communications throughout universities in Edinburgh and prompt vaccination clinics at affected campuses. Imported cases of measles pose a significant risk to university student cohorts who may be undervaccinated, include a large number of international students and have a highly mobile population. Public health departments should work closely with universities to promote MMR uptake and put in place mass vaccination plans to prevent rapidly spreading measles outbreaks in higher educational settings in future.

Dataset on significant risk factors for Type 1 Diabetes: A Bangladeshi perspective.

In this article, dataset and detailed data analysis results of Type-1 Diabetes has been given. Now-a-days Type-1 Diabetes is an appalling disease in Bangladesh. Total 306 person data (Case group- 152 and Control Group- 154) has been collected from Dhaka based on a specific questioner. The questioner includes 22 factors which were extracted by research studies. The association and significance level of factors has been elicited by using Data mining and Statistical Approach and shown in the Tables of this article. Moreover, parametric probability along with decision tree has been formed to show the effectiveness of the data was provided. The data can be used for future work like risk prediction and specific functioning on Type-1 Diabetes.

A Classification Model to Predict the Rate of Decline of Kidney Function.

The African American Study of Kidney Disease and Hypertension (AASK), a randomized double-blinded treatment trial, was motivated by the high rate of hypertension-related renal disease in the African-American population and the scarcity of effective therapies. This study describes a pattern-based classification approach to predict the rate of decline of kidney function using surface-enhanced laser desorption ionization/time of flight proteomic data from rapid and slow progressors classified by rate of change in glomerular filtration rate. An accurate classification model consisting of 7 out of 5,751 serum proteomic features is constructed by applying the logical analysis of data (LAD) methodology. On cross-validation by 10-folding, the model was shown to have an accuracy of 80.6 ± 0.11%, sensitivity of 78.4 ± 0.17%, and specificity of 78.5 ± 0.16%. The LAD discriminant is used to identify the patients in different risk groups. The LAD risk scores assigned to 116 AASK patients generated a receiver operating curves curve with AUC 0.899 (CI 0.845-0.953) and outperforms the risk scores assigned by proteinuria, one of the best predictors of chronic kidney disease progression.

The paired t test and beyond: Recommendations for testing the central tendencies of two paired samples in research on speech, language and hearing pathology.

PURPOSE: In this tutorial we review current practice in the analysis of data obtained in designs involving two dependent samples and evaluate two conventional statistics: the t test for paired samples and its non-parametric alternative, the Wilcoxon Signed Ranks test (WSR). It is a sequel to our tutorial on the analysis of designs with two independent samples on the basis of non-count data (Rietveld & van Hout, 2015). The frequency with which these statistics are used is assessed on the basis of publications on disordered communication in Clinical Linguistics & Phonetics, Journal of Communication Disorders and Journal of Speech, Language and Hearing Research for the time interval 2006-2015. We conclude with a number of recommendations for the analysis and presentation of data. CONCLUSIONS: Researchers should more consistently present the relevant characteristics of their data (means, medians, SD, skewness, tailedness, outliers etc.) and explicitly consider the assumptions that apply to their statistical methods, such as correlations between data obtained on two occasions, interactions between participants and treatment, and the symmetry of difference scores, many of which are hardly ever reported or even tested. Two recommendations are particularly relevant. First, the WSR is not a proper test for central tendencies as a replacement of the conventional t test for paired samples whenever assumptions about the dependent variable are in doubt. Second, researchers should choose statistical procedures on the basis of the null hypothesis (H0) to be tested and not primarily on the basis of the type of data (ordinal or interval). Two relevant H0's in the field of speech-language pathology are: (1) µ1=µ2 (the mean obtained in condition 1 is equal to the mean in condition 2) and (2) p=0.5, which says: the probability to obtain (for instance) higher scores in condition 2 than in condition 1 is 0.5. We recommend the permuted t test for paired samples to test the first H0 and the permuted Brunner-Munzel rank test to test the second.

Automated Design of Synthetic Cell Classifier Circuits Using a Two-Step Optimization Strategy.

Cell classifiers are genetic logic circuits that transduce endogenous molecular inputs into cell-type-specific responses. Designing classifiers that achieve optimal differential response between specific cell types is a hard computational problem because it involves selection of endogenous inputs and optimization of both biochemical parameters and a logic function. To address this problem, we first derive an optimal set of biochemical parameters with the largest expected differential response over a diverse set of logic circuits, and second, we use these parameters in an evolutionary algorithm to select circuit inputs and optimize the logic function. Using this approach, we design experimentally feasible microRNA-based circuits capable of perfect discrimination for several real-world cell-classification tasks. We also find that under realistic cell-to-cell variation, circuit performance is comparable to standard cross-validation performance estimates. Our approach facilitates the generation of candidate circuits for experimental testing in therapeutic settings that require precise cell targeting, such as cancer therapy.

Changes in the seroprevalence of cysticercosis in suspected patients in Chandigarh, India between 1998 and 2014: analysis of 17 years of data.

Changes in seroprevalence of cysticercosis diagnosed in Chandigarh, India between 1998 and 2014 were investigated by extraction and analysis of data from records held at the Postgraduate Institute of Medical Education and Research in Chandigarh. Among the total number of samples for which cysticercosis had been suspected during this period (N = 9650), 1716 (17·8%) were seropositive. Adults were more likely to be seropositive than children, and women were more likely to be seropositive than men. In addition to there being fewer patients with suspicion of cysticercosis over the data analysis period, the proportion of patients seropositive also reduced significantly. Despite these reductions, which are probably associated with improved infrastructure and sanitation within Chandigarh, and despite meat consumption being relatively rare in this area, the extent of cysticercosis in this population remains problematic. Further efforts should be made to reduce transmission of this infection, with particular emphasis on women. Such efforts should follow the One Health concept, and involve medical efforts (including diagnosis and treatment of T. solium tapeworm carriers), veterinary efforts directed towards meat inspection and prevention of infection of pigs, and environmental health and sanitation engineers (to minimize environmental contamination with human waste).

Precursors to the PANSS: The BPRS and its progenitors.

The systematic assessment of signs and symptoms of psychopathology has roots that date back to rating scale development that began in the 1950s. This article reviews some of those rating scales. The focus is on the Brief Psychiatric Rating Scale, which is the most important precursor of the Positive and Negative Symptom Rating Scale.

A 10-year cohort analysis of routine paediatric ART data in a rural South African setting.

South Africa's paediatric antiretroviral therapy (ART) programme is managed using a monitoring and evaluation tool known as TIER.Net. This electronic system has several advantages over paper-based systems, allowing profiling of the paediatric ART programme over time. We analysed anonymized TIER.Net data for HIV-infected children aged <15 years who had initiated ART in a rural district of South Africa between 2005 and 2014. We performed Kaplan-Meier survival analysis to assess outcomes over time. Records of 5461 children were available for analysis; 3593 (66%) children were retained in care. Losses from the programme were higher in children initiated on treatment in more recent years (P < 0·0001) and in children aged ≤1 year at treatment initiation (P < 0·0001). For children aged <3 years, abacavir was associated with a significantly higher rate of loss from the programme compared to stavudine (hazard ratio 1·9, P < 0·001). Viral load was suppressed in 48-52% of the cohort, with no significant change over the years (P = 0·398). Analysis of TIER.Net data over time provides enhanced insights into the performance of the paediatric ART programme and highlights interventions to improve programme performance.