Procedure and Ethics of Triage: Rationing Healthcare During Pandemics and Disasters
Bibliographic record
Abstract
The demand for healthcare services is likely to often exceed supply during pandemics and disasters, as we have experienced during the COVID-19 pandemic recently across the globe; Bangladesh is not an exception. In hospital settings with such constraining conditions especially in low-income countries like Bangladesh, institutions and individual providers of healthcare must use some moral framework for distributing the available resources efficiently and equitably during critical times. Triage is a military term in origin, being used to describe the prioritization of wounded soldiers and the use of available medical resources for maximal efficiency. Commonly recognized examples of triage include prehospital, catastrophic, emergency department, intensive care, waiting list (e.g., for lifesaving treatments such as surgical operation, dialysis, and organ transplants), and in battlefield casualties. Triage has the ability to substantially decrease mortality and morbidity by providing timely and specific care for critically ill patients on a priority basis. This paper aims to discuss triage procedure and ethical debates behind practice of triage during the pandemics and disasters. Mugda Med Coll J. 2025; 8(1): 60-65
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".