Resource Allocation After a Nuclear Detonation Incident: Unaltered Standards of Ethical Decision Making
Bibliographic record
Abstract
This article provides practical ethical guidance for clinicians making decisions after a nuclear detonation, in advance of the full establishment of a coordinated response. We argue that the utilitarian maxim of the greatest good for the greatest number, interpreted only as "the most lives saved," needs refinement. We take the philosophical position that utilitarian efficiency should be tempered by the principle of fairness in making decisions about providing lifesaving interventions and palliation. The most practical way to achieve these goals is to mirror the ethical precepts of routine clinical practice, in which 3 factors govern resource allocation: order of presentation, patient's medical need, and effectiveness of an intervention. Although these basic ethical standards do not change, priority is given in a crisis to those at highest need in whom interventions are expected to be effective. If available resources will not be effective in meeting the need, then it is unfair to expend them and they should be allocated to another patient with high need and greater expectation for survival if treated. As shortage becomes critical, thresholds for intervention become more stringent. Although the focus of providers will be on the victims of the event, the needs of patients already receiving care before the detonation also must be considered. Those not allocated intervention must still be provided as much appropriate comfort, assistance, relief of symptoms, and explanations as possible, given the available resources. Reassessment of patients' clinical status and priority for intervention also should be conducted with regularity.
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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 | 0.335 | 0.345 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.072 |
| Scholarly communication | 0.028 | 0.022 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.031 | 0.045 |
| Insufficient payload (model declined to judge) | 0.002 | 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".