Physicians, Thou Shalt Ration: The Necessary Role of Bedside Rationing in Controlling Healthcare Costs
Bibliographic record
Abstract
Physicians are often asked to be "gatekeepers," determining their patients' access to medical therapies and technologies.At the same time, most physicians have been taught that they should act as patient advocates, pursuing patients' best interests regardless of cost.This paper reviews moral arguments ethicists have made for and against "bedside rationing."It argues that healthcare rationing is appropriate in order to help control healthcare costs, and that rationing decisions made at the bedside by physicians must be part of the rationing system.A system that attempts to control costs by mandating an elaborate set of rules would be burdensome, and many physicians would find ways around the rules anyway.Physicians are deeply conflicted about their roles in cost-containment.Some of the conflict has to do with discomfort over the concept of "rationing," but they are also in Dr. Ubel is a Robert Wood Johnson Foundation Generalist Physician Faculty Scholar, recipient of a career development award in health services research from the Department of Veterans Affairs, and recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE).
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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 | 0.009 | 0.034 |
| 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 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".