Supporting decision-making on allocation of ICU beds and ventilators in pandemics
Bibliographic record
Abstract
Abstract As the world struggles with the COVID-19 pandemic, health service demands have increased to a point where healthcare resources may prove inadequate to meet demand. Guidelines and tools on how to best allocate intensive care beds and ventilators developed during previous epidemics can assist clinicians and policy-makers to make consistent, objective and ethically sounds decisions about resource allocation when healthcare rationing is inevitable. This scoping review of 62 published guidelines, triage protocols, consensus statements and prognostic tools from crisis and non-crisis situations sought to identify a multiplicity of objective factors to inform healthcare rationing of critical care and ventilator care. It also took ethical considerations into account. Prognostic indicators and other decision tools presented here can be combined to create locally-relevant triage algorithms for clinical services and policy makers deciding about allocation of ICU beds and ventilators during a pandemic. Community awareness of the triage protocol is recommended to build trust and alleviate anxiety among the public. This review provides a unique resource and is intended as a discussion starter for clinical services and policy makers to consider formalising an objective triage consensus document that fits the local context. Take-home message An evidence-based catalogue of objective variables from 62 published resources tested in crisis and non-crisis situations can help clinicians make locally relevant triage decisions on ICU and ventilator allocation in inevitable COVID-19 health rationing.
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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 | 0.127 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".