A framework for critical care triage during a major surge in critical illness
Bibliographic record
Abstract
During the COVID-19 pandemic, many jurisdictions experienced surges in demand for critical care that strained or overwhelmed their healthcare system's ability to respond. A major surge necessitates a deviation from usual practices, including difficult decisions about how to allocate critical care resources. We present a framework to guide these decisions in the hope of saving the most lives as ethically as possible, while concurrently respecting, protecting, and fulfilling legal and human rights obligations. It was developed in Ontario in 2020-2021 through an iterative consultation process with diverse participants, but was adopted in other jurisdictions with some modifications. The framework features three levels of triage depending on the degree of the surge, and a system for prioritizing patients based on their short-term mortality risk following the onset of critical illness. It also includes processes aimed at promoting consistency and fairness across a region where many hospitals are expected to apply the same framework. No triage framework should ever be considered "final," and there is a need for further research to examine ethical issues related to critical care triage and to increase the extent and quality of evidence to inform critical care triage.
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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 | 0.055 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".