Health economic evaluation: Important principles and methodology
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To discuss health economic evaluation and improve the understanding of common methodology. RESULTS: This article discusses the methodology for the following types of economic evaluations: cost-minimization, cost-effectiveness, cost-utility, cost-benefit, and economic modeling. Topics include health-state utility measures, the quality-adjusted life year (QALY), uncertainty analysis, discounting, decision tree analysis, and Markov modeling. CONCLUSION: Economic evaluation is the comparative analysis of alternative courses of action in terms of both their costs and consequences. With increasing health care expenditure and limited resources, it is important for physicians to consider the economic impact of their interventions. Understanding common methodology involved in health economic evaluation will improve critical appraisal of the literature and optimize future economic evaluations.
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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 | 0.174 | 0.256 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".