¿Qué queda de mí?

Este libro es una reclamación a quienes hemos sido, somos o seremos docentes. A quienes no hemos respetado a las personas que se han puesto junto a nosotros y nosotras, confiando su bien más preciado: la libertad. Estas páginas denuncian cada vez que convertimos una visión en la visión, una emoción en la emoción, un saber en el saber, un comportamiento en el comportamiento. Es un grito contra la imposición, la normalización, la neutralización y la universalización de una perspectiva particular. Una pugna contra cada proceso que no se ha conectado con las vidas de los aprendices. Un texto colaborativo realizado por alumnado de Educación y Cambio Social en el Grado en Educación Infantil de la Universidad de Málaga y coordinado por Ignacio Calderón Almendros

Logistic Regression coefficients and odd ratios for the PCR set.

<p>This table shows the coefficients of the logistic regression model that results from backward variable selection with AIC, applied on the PCR and non-PCR sets. The coefficients of each model are together with the P-value of the Wald test with a null hypothesis consisting of the maximum likelihood estimate of the coefficient being equal to zero, the odd-ratios for a unit change in each variable, and their 95% confidence interval.</p

The ten variables that have the highest Mutual Information content with EVD outcome, as ranked with MIC.

<p>This set of 10 variables include virus load (PCR), temperature, aspartate aminotransferase (AST), Alkaline Phosphatase (ALK), Alanine Aminotransferase (ALT), Creatinine (CRE), Total Carbon Dioxide (tCO2), heart rate, diarrhea, weakness, and vomit. The plot in panel A represents the eikosograms for all 10 variables, generated from the clinical records of 65 EVD patients between 10 and 50 years of age and known outcome. An eikosogram is a plot that represents the conditional probabilities of one variable (in this case outcome) as a function of the conditioning variable. Staircase shapes in an eikosogram are indicative of association. The plot in panel B shows the ranking of the 10 variables by their MIC score with outcome.</p

Summary of all models generated with and without PCR data.

<p>Each point in scatter plots (a) and (c) represents a predictive model, defined by a particular selection of input variables and a prediction algorithm (LR, ANN, DT, or SVM), trained and tested 100 times. The mean F1-score (weighted average of the precision and sensitivity) calculated over the 100 testing iterations is shown in horizontal axis, while the standard deviation of the F1-score is represented in the vertical axis. The size of the point is proportional to the number of input variables. The bar plots on the right (panels b and d) show the number of times each variable appears in a predictor with mean F1-score above 0.9. Panels A and B represents the models including PCR data, while C and D, represent those without.</p

Optimistic-bias estimation.

<p>The optimistic bias for the AUC scores of all top PCR (a) and non-PCR (b) predictors were estimated using bootstrap sampling method, averaging the difference between the AUC on the original data and the bootstrap samples over 100 iterations. The scatter plots show the original AUC scores for each model in the horizontal axis, the mean bias on the vertical axis, and the standard deviation of the bias as the error bar. Panels (c) and (d) show the dependency of the optimistic bias as a function of the number of imputed copies, for a logistic regression model that results of applying backward variable selection on the PCR (c) and non-PCR sets of variables (d). The backward selection algorithm was run 10 times for each number of imputed copies, and the mean bias over the 10 iterations is presented, with the standard deviation as the error bars. The bias is quite large when only one imputation is computed, but it decreases exponentially towards 0.01 as the number of multiple imputations increases. The red lines in all plots represent least squares fitted curves, using a linear function in (a, b), and an exponential curve in (c, d), thus highlighting the nature of the dependency of the optimistic bias as a function of the AUC, and the number of imputed copies.</p

Case counts in the dataset.

<p>The flowchart indicates the total number of positive, confirmed EVD in the original dataset, from which we took only those corresponding to patients with ages between 10 and 50. From those, only 65 have known outcome and could be used for analysis. In the bottom part of this diagram, the numbers of cases within the last 65 that contain clinical chart (24), metabolic panel (47), and virus load data (58) are represented by fill rectangles. The resulting missing data pattern illustrates that only a few patients had known information across all categories.</p

Logistic Regression coefficients and odd ratios for the non-PCR set.

<p>This table shows the coefficients of the logistic regression model that results from backward variable selection with AIC, applied on the non-PCR sets. The interpretation of coefficients, P-values, odd-ratios and confidence intervals is the same as in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004549#pntd.0004549.t001" target="_blank">Table 1</a>.</p