Bibliographic record
Abstract
The cost-effectiveness threshold in health care systems with a constrained budget should be determined by the cost-effectiveness of displacing health care services to fund new interventions. Using comparative statics, we review some potential determinants of the threshold, including the budget for health care, the demand for existing health care interventions, the technical efficiency of existing interventions, and the development of new health technologies. We consider the anticipated direction of impact that would affect the threshold following a change in each of these determinants. Where the health care system is technically efficient, an increase in the health care budget unambiguously raises the threshold, whereas an increase in the demand for existing, non-marginal health interventions unambiguously lowers the threshold. Improvements in the technical efficiency of existing interventions may raise or lower the threshold, depending on the cause of the improvement in efficiency, whether the intervention is already funded, and, if so, whether it is marginal. New technologies may also raise or lower the threshold, depending on whether the new technology is a substitute for an existing technology and, again, whether the existing technology is marginal. Our analysis permits health economists and decision makers to assess if and in what direction the threshold may change over time. This matters, as threshold changes impact the cost-effectiveness of interventions that require decisions now but have costs and effects that fall in future periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".