Allocation of Ventilators in a Public Health Disaster
Bibliographic record
Abstract
BACKGROUND: In a public health emergency, many more patients could require mechanical ventilators than can be accommodated. METHODS: To plan for such a crisis, the New York State Department of Health and the New York State Task Force on Life and the Law convened a workgroup to develop ethical and clinical guidelines for ventilator triage. RESULTS: The workgroup crafted an ethical framework including the following components: duty to care, duty to steward resources, duty to plan, distributive justice, and transparency. Incorporating the ethical framework, the clinical guidelines propose both withholding and withdrawing ventilators from patients with the highest probability of mortality to benefit patients with the highest likelihood of survival. Triage scores derive from the sepsis-related organ failure assessment system, which assigns points based on function in 6 basic medical domains. Triage may not be implemented by a facility without clear permission from public health authorities. CONCLUSIONS: New York State released the draft guidelines for public comment, allowing for revision to reflect both community values and medical innovation. This ventilator triage system represents a radical shift from ordinary standards of care, and may serve as a model for allocating other scarce resources in disasters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".