An Introduction to Economic Evaluation: What's in a Name?
Bibliographic record
Abstract
OBJECTIVE: This paper describes the main types of economic evaluation techniques. METHOD: To examine the strengths and limitations of different types of economic evaluations, we used a hypothetical example to review the reasoning underlying each method and to illustrate when it is appropriate to use each method. RESULTS: The choice of economic evaluation method reflects a decision about what should represent "success" and how success should be valued. Measures of benefit and cost must be considered systematically and simultaneously. Claiming that a new treatment is cost-effective requires making a value judgment based on the personal beliefs of the claimant. Even when cost and effect data are objective, a verdict of cost-effective is subjective. The conclusions of an economic study can change significantly, depending on which patient outcome is used to measure success. CONCLUSIONS: Clinicians must be sure that important patient outcomes are not excluded from economic evaluations. Economic evaluation is a process designed to produce an estimate rather than a decision. New treatment can be more costly and still be cost-effective (if the extra benefit is valued more than the extra cost to produce it). However, since economic evaluation does not explicitly consider a decision maker's available budget, a new treatment can be deemed cost-effective but too expensive to approve.
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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 | 0.067 | 0.180 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".