Recommendations on COVID‐19 triage: international comparison and ethical analysis
Bibliographic record
Abstract
On March 11, 2020 the World Health Organization classified COVID-19, caused by Sars-CoV-2, as a pandemic. Although not much was known about the new virus, the first outbreaks in China and Italy showed that potentially a large number of people worldwide could fall critically ill in a short period of time. A shortage of ventilators and intensive care resources was expected in many countries, leading to concerns about restrictions of medical care and preventable deaths. In order to be prepared for this challenging situation, national triage guidance has been developed or adapted from former influenza pandemic guidelines in an increasing number of countries over the past few months. In this article, we provide a comparative analysis of triage recommendations from selected national and international professional societies, including Australia/New Zealand, Belgium, Canada, Germany, Great Britain, Italy, Pakistan, South Africa, Switzerland, the United States, and the International Society of Critical Care Medicine. We describe areas of consensus, including the importance of prognosis, patient will, transparency of the decision-making process, and psychosocial support for staff, as well as the role of justice and benefit maximization as core principles. We then probe areas of disagreement, such as the role of survival versus outcome, long-term versus short-term prognosis, the use of age and comorbidities as triage criteria, priority groups and potential tiebreakers such as 'lottery' or 'first come, first served'. Having explored a number of tensions in current guidance, we conclude with a suggestion for framework conditions that are clear, consistent and implementable. This analysis is intended to advance the ongoing debate regarding the fair allocation of limited resources and may be relevant for future policy-making.
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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 | 0.457 | 0.652 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".