Objective: To compare the predictive performance of residents, senior intensive care unit physicians and surrogates early during intensive care unit stays and to evaluate whether different presentations of prognostic data (probability of survival versus probability of death) influenced their performance.
Methods: We questioned surrogates and physicians in charge of critically ill patients during the first 48 hours of intensive care unit admission on the patient's probability of hospital outcome. The question framing (i.e., probability of survival versus probability of death during hospitalization) was randomized. To evaluate the predictive performance, we compared the areas under the ROC curves (AUCs) for hospital outcome between surrogates and physicians' categories. We also stratified the results according to randomized question framing.
Results: We interviewed surrogates and physicians on the hospital outcomes of 118 patients. The predictive performance of surrogate decisionmakers was significantly lower than that of physicians (AUC of 0.63 for surrogates, 0.82 for residents, 0.80 for intensive care unit fellows and 0.81 for intensive care unit senior physicians). There was no increase in predictive performance related to physicians' experience (i.e., senior physicians did not predict outcomes better than junior physicians). Surrogate decisionmakers worsened their prediction performance when they were asked about probability of death instead of probability of survival, but there was no difference for physicians.
Conclusion: Different predictive performance was observed when comparing surrogate decision-makers and physicians, with no effect of experience on health care professionals' prediction. Question framing affected the predictive performance of surrogates but not of physicians.
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http://dx.doi.org/10.5935/0103-507X.20220019-pt | DOI Listing |
Expert Opin Drug Metab Toxicol
January 2025
Institut de R&D Servier, Paris-Saclay, F-91190 Gif-sur-Yvette, France.
Introduction: Drug-mediated inhibition of bile salt efflux transporters may cause liver injury. In vitro prediction of drug effects toward canalicular and/or sinusoidal efflux of bile salts from human hepatocytes is therefore a major issue, which can be addressed using liver cell-based assays.
Area Covered: This review, based on a thorough literature search in the scientific databases PubMed and Web of Science, provides key information about hepatic transporters implicated in bile salt efflux, the human liver cell models available for investigating functional inhibition of bile salt efflux, the different methodologies used for this purpose, and the modes of expression of the results.
Abdom Radiol (NY)
January 2025
Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China.
Background: To develop and validate a clinical-radiomics model for preoperative prediction of lymphovascular invasion (LVI) in rectal cancer.
Methods: This retrospective study included data from 239 patients with pathologically confirmed rectal adenocarcinoma from two centers, all of whom underwent MRI examinations. Cases from the first center (n = 189) were randomly divided into a training set and an internal validation set at a 7:3 ratio, while cases from the second center (n = 50) constituted the external validation set.
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
Bioinformatics
January 2025
Biocomputing Group, University of Bologna, Italy.
Motivation: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations.
Results: In this work we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein ΔΔG upon both single- and multi-point variations.
Bioinformatics
January 2025
Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
Motivation: Understanding the associations between traits and microbial composition is a fundamental objective in microbiome research. Recently, researchers have turned to machine learning (ML) models to achieve this goal with promising results. However, the effectiveness of advanced ML models is often limited by the unique characteristics of microbiome data, which are typically high-dimensional, compositional, and imbalanced.
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