Identifying Prioritization Criteria to Supplement Critical Care Triage Protocols for the Allocation of Ventilators during a Pandemic Influenza
Bibliographic record
Abstract
The purpose of this study was to identify supplementary criteria to provide direction when the Ontario Health Plan for an Influenza Pandemic (OHPIP) critical care triage protocol is rendered insufficient by its inability to discriminate among patients assessed as urgent, and there are insufficient critical care resources available to treat those in that category. To accomplish this task, a Supplementary Criteria Task Force for Critical Care Triage was struck at the University of Toronto Joint Centre for Bioethics. The task force reviewed publically available protocols and policies on pandemic flu planning, identified 13 potential triage criteria and determined a set of eight key ethical, legal and practical considerations against which it assessed each criterion. An online questionnaire was distributed to clinical, policy and community stakeholders across Canada to obtain feedback on the 13 potential triage criteria toward selecting those that best met the eight considerations. The task force concluded that the balance of arguments favoured only two of the 13 criteria it had identified for consideration: first come, first served and random selection. The two criteria were chosen in part based on a need to balance the clearly utilitarian approach employed in the OHPIP with equity considerations. These criteria serve as a defensible "fail safe" mechanism for any triage protocol.
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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 | 0.110 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".