Understanding Harris’ understanding of CEA: is cost effective resource allocation undone?
Bibliographic record
Abstract
We summarise and evaluate Harris' criticisms of cost-effectiveness analysis (CEA) and the alternative processes he commends to health care decision makers. In contrast to CEA, Harris' asserts that individuals have a right to life-saving treatment that cannot be denied on the basis of their capacity to benefit. We conclude that, whilst Harris' work has challenged the proponents of CEA and quality-adjusted life years to be explicit about the method's indirect discriminatory characteristics, his arguments ignore important questions about what 'lives saved' mean. Harris also attempts to avoid opportunity cost by advocating the same chance of treatment for every person desiring treatment. Using a simple example, we illustrate that an 'equal chances' lottery is not in the interest of any patient, as it reduces the chance of treatment for all patients by leaving some of the health budget unspent.
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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 | 0.031 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.034 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".