The importance of age in allocating health care resources: does intervention-type matter?
Bibliographic record
Abstract
BACKGROUND: Recent proposals to reform cost-effectiveness analysis (CEA) by weighting health benefits [(Quality-adjusted life-years) QALYs] by recipients' age are based on studies examining age-related preferences in life-saving contexts. We investigated whether the perceived importance of age in resource allocation decisions differs among intervention-types. METHODS: 160 individuals were recruited from a cafeteria of a university medical centre and asked to choose between hypothetical health care programmes. Scenario A described two programmes treating life-threatening conditions and Scenario B two programmes providing palliative care. Programmes were identical except in average patient age (35 versus 65). Respondents also directly rated the importance of age for allocating resources for six types of interventions. RESULTS: Responses for the life-saving scenario favoured younger age groups while those for the palliative care scenario showed no age preference. The difference between scenarios was statistically significant. When directly rating the importance of age in allocating treatment resources, people placed greatest importance on age in treating infertility and life-saving, and least importance in treating depression. DISCUSSION: The importance people place on age as a resource allocation criterion depends on the clinical context. As QALYs serve as a common measure of health benefits for all intervention types, age weighting of QALYs is premature.
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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 | 0.082 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".