The importance of age in allocating health care resources: does intervention-type matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it