Ethical considerations of age-based allocation of intensive care treatments amid the COVID-19 outbreak
Bibliographic record
Abstract
With the COVID-19 outbreak severely overwhelming healthcare systems worldwide, countries must decide on allocation criteria for scarce intensive care resources such as ventilators, leaving some without life-saving treatment. Groups such as the Italian College of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI) have suggested using age as allocation criteria, prioritizing the young over the elderly. In judging the morality of such criteria, different ethical frameworks must be applied. From a utilitarian perspective, age-based allocation ensures “the greatest good” – that those with greater “therapeutic success” or “quality-of-life” get access to intensive treatment. However, age poorly predicts prognostic outcomes, and quality-of-life measures are inherently value-laden. From a contractarian view, a morally justifiable action is one made in ignorance of one’s own stake in the outcome. In this lens, age-based allocation is justified since it maximizes the most life-years for the most people. However, it relies on the same flawed assumptions as utilitarianism. From a prioritarian view, age-based allocation ensures that the rights of the young to live out a “normal life span” are respected. However, such judgements ignore the positive experiences of later life and cannot be made on a patient’s behalf. Through a deontological lens, age-based allocation is discriminatory as it views elderly people as means to an end rather than individual agents. Ultimately, the rationing criteria a society uses reflects its values, with age limitations implicitly devaluating the elderly. Therefore, allocation guidelines should deemphasize age in favor of more predictive and less discriminatory measures like multimorbidity or frailty.
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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 | 0.145 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.022 | 0.018 |
| 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".