Multiple-input multiple-output causal strategies for gene selection

Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

COVID-19-related absence among surgeons: development of an international surgical workforce prediction model

Background: During the initial COVID-19 outbreak up to 28.4 million elective operations were cancelled worldwide, in part owing to concerns that it would be unsustainable to maintain elective surgery capacity because of COVID-19-related surgeon absence. Although many hospitals are now recovering, surgical teams need strategies to prepare for future outbreaks. This study aimed to develop a framework to predict elective surgery capacity during future COVID-19 outbreaks. Methods: An international cross-sectional study determined real-world COVID-19-related absence rates among surgeons. COVID-19-related absences included sickness, self-isolation, shielding, and caring for family. To estimate elective surgical capacity during future outbreaks, an expert elicitation study was undertaken with senior surgeons to determine the minimum surgical staff required to provide surgical services while maintaining a range of elective surgery volumes (0, 25, 50 or 75 per cent). Results Based on data from 364 hospitals across 65 countries, the COVID-19-related absence rate during the initial 6 weeks of the outbreak ranged from 20.5 to 24.7 per cent (mean average fortnightly). In weeks 7–12, this decreased to 9.2–13.8 per cent. At all times during the COVID-19 outbreak there was predicted to be sufficient surgical staff available to maintain at least 75 per cent of regular elective surgical volume. Overall, there was predicted capacity for surgeon redeployment to support the wider hospital response to COVID-19. Conclusion: This framework will inform elective surgical service planning during future COVID-19 outbreaks. In most settings, surgeon absence is unlikely to be the factor limiting elective surgery capacity