État des lieux de la démarche éthique pour l'admission en réanimation des patients covid : étude ETHICUS COVID-19. étude transversale observationnelle
Bibliographic record
Abstract
Introduction: The SARS-CoV2 pandemic has put the French healthcare system under strain, leading in some regions and at some times to a mismatch between healthcare provision and demand. Against this backdrop, it has been necessary to introduce triage criteria and tools to organize the admission of patients to intensive care units. The aim is to maximize the number of lives saved by optimizing the resources available. Objectives: The primary objective of our study was to determine the presence or otherwise of procedures (in the form of triage protocols) to assist admission to critical care units throughout France in the context of the COVID19 pandemic. We then analyzed which elements were included in these protocols. Finally, we also felt it would be useful to describe the ethical dilemmas faced by caregivers and to assess their psychological impact. Study design: Our study is a cross-sectional, observational study. Data were collected between 25/09/2020 and 28/12/2020. Materials and methods: The study was conducted by sending a standardized questionnaire by email. The questionnaire was sent to a large population of critical care doctors. After collecting socio-demographic information, the questionnaire obtained data on intensive care unit admission procedures in the various centers of the carers questioned. Results: A total of 253 responses were analysed. 29.2% (n=74) of respondents reported that their department has an admission procedure. Of the existing procedures, the data used included comorbidities (84%), a frailty scale (80%), age (62%), the SOFA score on admission (18%) and the ONTARIO score (3%). In addition, 79% (n=199) of respondents said that their institution had an ethics committee. The problematic situations perceived by respondents were as follows: the ban on family visits (80%), the prioritization of patients in the face of a shortage of beds (42%) and the lack of equipment (37%). Conclusion: In our study, few healthcare professionals reported the existence of intensive care unit admission procedures within their department. Existing procedures included criteria comparable to those recommended worldwide. These results should be interpreted in the light of how early in the pandemic our study was carried out. Few caregivers claim to have had recourse to the ethics committee in their hospital.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".