Validation of a Clinical Scoring System for Outcome Prediction in Dogs with Acute Kidney Injury Managed by Hemodialysis.

BackgroundA scoring system for outcome prediction in dogs with acute kidney injury (AKI) recently has been developed but has not been validated.HypothesisThe scoring system previously developed for outcome prediction will accurately predict outcome in a validation cohort of dogs with AKI managed with hemodialysis.AnimalsOne hundred fifteen client-owned dogs with AKI.MethodsMedical records of dogs with AKI treated by hemodialysis between 2011 and 2015 were reviewed. Dogs were included only if all variables required to calculate the final predictive score were available, and the 30-day outcome was known. A predictive score for 3 models was calculated for each dog. Logistic regression was used to evaluate the association of the final predictive score with each model's outcome. Receiver operating curve (ROC) analyses were performed to determine sensitivity and specificity for each model based on previously established cut-off values.ResultsHigher scores for each model were associated with decreased survival probability (P < .001). Based on previously established cut-off values, 3 models (models A, B, C) were associated with sensitivities/specificities of 73/75%, 71/80%, and 75/86%, respectively, and correctly classified 74-80% of the dogs.Conclusions and clinical relevanceAll models were simple to apply and allowed outcome prediction that closely corresponded with actual outcome in an independent cohort. As expected, accuracies were slightly lower compared with those from the previously reported cohort used initially to develop the models

Imitation and the Evolution of Walrasian Behavior: Theoretically Fragile but Behaviorally Robust

A well-known result by Vega-Redondo (1997) implies that in symmetric Cournot oligopoly, imitation leads to the Walrasian outcome where price equals marginal cost. In this paper, we show that this result is not robust to the slightest asymmetry in fixed costs. Instead of obtaining the Walrasian outcome as unique prediction, every outcome where agents choose identical actions will be played some fraction of the time in the long run. We then conduct experiments to check this fragility. We obtain that, contrary to the theoretical prediction, the Walrasian outcome is still a good predictor of behavior.evolutionary game theory, stochastic stability, imitation, Cournot markets, information, experiments, simulations

Imitation and the Evolution of Walrasian Behavior: Theoretically Fragile but Behaviorally Robust

A well-known result by Vega-Redondo implies that in symmetric Cournot oligopoly, imitation leads to the Walrasian outcome where price equals marginal cost. In this paper we show that this result is not robust to the slightest asymmetry in fixed costs. Instead of obtaining the Walrasian outcome as unique prediction, every outcome where agents choose identical actions will be played some fraction of the time in the long run. We then conduct experiments to check this fragility. We obtain that, contrary to the theoretical prediction, the Walrasian outcome is still a good predictor of behavior.Evolutionary game theory; Stochastic stability; Imita- tion; Cournot markets; Information; Experiments; Simulations

Outcome Prediction for Unipolar Depression

Although effective drug and non-drug treatment for unipolar depressive illness exist, different individuals respond differently to different treatments. It is not uncommon for a given patient to lw switched several times from one treatment to another until an effective remedy for that particular patient is found. This process is costly in terms of time, money and suffering. It is thus desirable to determine at the outset the likdy response of a patient to the available treatments, so that the optimal one can be selected. Although prior attempts at outcome prediction with linear regression models have failed, recent work on this problem has indicated that the nonlinear predictive techniques of backpropagation and quadratic regression call account for a significant proportion of the variance in the data. The present research applies the nonlinear predictive technique of kernel regression to this problcrn, and employs cross-validation to test the ability of the resulting model to extract, from extremely noisy dinical data, information with predictive value. The importance of comparison with a suitable null hypothesis is illustrated.Office of Naval Research (N00014-95-1-0409

The Strategy of Experts for Repeated Predictions

We investigate the behavior of experts who seek to make predictions with maximum impact on an audience. At a known future time, a certain continuous random variable will be realized. A public prediction gradually converges to the outcome, and an expert has access to a more accurate prediction. We study when the expert should reveal his information, when his reward is based on a proper scoring rule (e.g., is proportional to the change in log-likelihood of the outcome). In Azar et. al. (2016), we analyzed the case where the expert may make a single prediction. In this paper, we analyze the case where the expert is allowed to revise previous predictions. This leads to a rather different set of dilemmas for the strategic expert. We find that it is optimal for the expert to always tell the truth, and to make a new prediction whenever he has a new signal. We characterize the expert's expectation for his total reward, and show asymptotic limitsComment: To appear in WINE 201

A Statistical Model for Stroke Outcome Prediction and Treatment Planning

Stroke is a major cause of mortality and long--term disability in the world. Predictive outcome models in stroke are valuable for personalized treatment, rehabilitation planning and in controlled clinical trials. In this paper we design a new model to predict outcome in the short-term, the putative therapeutic window for several treatments. Our regression-based model has a parametric form that is designed to address many challenges common in medical datasets like highly correlated variables and class imbalance. Empirically our model outperforms the best--known previous models in predicting short--term outcomes and in inferring the most effective treatments that improve outcome