How Does Cost-Effectiveness Analysis Inform Health Care Decisions?
Bibliographic record
Abstract
Cost-effectiveness analysis (CEA) provides a formal assessment of trade-offs involving benefits, harms, and costs inherent in alternative options. CEA has been increasingly used to inform public and private organizations' reimbursement decisions, benefit designs, and price negotiations worldwide. Despite the lack of centralized efforts to promote CEA in the United States, the demand for CEA is growing. This article briefly reviews the history of CEA in the United States, highlights advances in practice guidelines, and discusses CEA's ethical challenges. It also offers a way forward to inform health care decisions.
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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 | 0.232 | 0.605 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".