Principles of resource allocation and triage during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic confronted Canadians with the fact that our health care systems may not always have enough to go around. Critical care resources, specifically, were stretched far beyond the limits of what was thought possible. In the spring of 2021, the exponential growth of patients with COVID-19 brought Ontario’s ICUs frighteningly near the breaking point. When a health system’s resources are overwhelmed by the demands placed upon them, allocation of scarce resources is typically performed by triage — a formalized system to determine who receives critical care resources and who does not. In this commentary, we will explain the rationale for the use of a formal triage protocol during times of resource scarcity; review the ethical foundations of an approach to resource allocation; outline the process of triage protocol development in Ontario during the COVID-19 pandemic, and highlight some lessons learned for the future.
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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 | 0.131 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.072 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".