Fabricius Marquez (massbowl2)

Survival analysis is a widely used method to establish a connection between a time to event outcome and a set of potential covariates. Accurately predicting the time of an event of interest is of primary importance in survival analysis. Many different algorithms have been proposed for survival prediction. However, for a given prediction problem it is rarely, if ever, possible to know in advance which algorithm will perform the best. In this paper we propose two algorithms for constructing super learners in survival data prediction where the individual algorithms are based on proportional hazards. Enasidenib purchase A super learner is a flexible approach to statistical learning that finds the best weighted ensemble of the individual algorithms. Finding the optimal combination of the individual algorithms through minimizing cross-validated risk controls for over-fitting of the final ensemble learner. Candidate algorithms may range from a basic Cox model to tree-based machine learning algorithms, assuming all candidate algorithms are based on the proportional hazards framework. The ensemble weights are estimated by minimizing the cross-validated negative log partial likelihood. We compare the performance of the proposed super learners with existing models through extensive simulation studies. In all simulation scenarios, the proposed super learners are either the best fit or near the best fit. The performances of the newly proposed algorithms are also demonstrated with clinical data examples.BACKGROUND Exergames have the potential to provide an accessible, remote approach for post stroke upper extremity (UE) rehabilitation. However, the use of exergames without any follow-up by a health professional could lead to compensatory movements during the exercises, inadequate choice of difficulty level, exercises not being completed and lack of motivation to pursue exercise program, thereby decreasing their benefits. Combining telerehabilitation with exergames could allow continuous adjustment of the exercises and monitoring of the participant completion and adherence. Currently, there is limited evidence regarding the feasibility or efficacy of combining telerehabilitation and exergames for stroke rehabilitation. OBJECTIVE 1) To determine the preliminary efficacy of using telerehabilitation combined with exergames on UE motor recovery, function, quality of life and motivation, in participants with chronic stroke, compared with conventional therapy (the graded repetitive arm supplementary program) 2) To CONCLUSIONS This paper describes the protocol underlying the study of a telerehabilitation-exergame technology to contribute to understanding its feasibility and preliminary efficacy for UE stroke rehabilitation. CLINICALTRIAL Clinicaltrials.gov (NCT03759106).INTRODUCTION Delivering bad news is a difficult task for physicians, and medical schools do not always prepare future physicians for this inevitable task. OBJECTIVE To examine training in breaking bad news, to improve medical students' competence and confidence in dealing with this important aspect of clinical practice. METHODS An exploratory study using a qualitative approach was done at a Brazilian public university's medical school, which receives 30 medical students per semester. Two focus groups were conducted in 2018, with 15 students per group, before and after the training. The intervention consisted of a 6-month (4 h/wk) course about breaking bad news offered to 30 third-year medical students. The communication course included the perspectives of health care professionals, patients, and their families; the SPIKES protocol and the "ABCDE" mnemonic for delivering bad news; general guidelines; and role-playing/simulation strategies to improve students' skills and reduce their personal limitations. RESULnd skills acquired gave them tools needed to deliver bad news in their future clinical practice.INTRODUCTION Older age is a melanoma risk factor. Elderly individuals are lik