Cost‐Effectiveness of Epilepsy Therapy: How Should Treatment Effects Be Measured?
Bibliographic record
Abstract
Economic evaluations aim to inform policy makers about the cost-effectiveness of different therapies so that limited health care resources may be allocated efficiently. For such evaluations, the effects of therapy must be captured by measures that are reliable, valid, and clinically meaningful. Mortality and changes in disease activity (e.g., seizure freedom, reduction in seizure frequency) may be reliable and valid, but their clinical meaning is not always apparent. Changes in widely used "quality-of-life" measures can quantify therapeutic effects, but the value of such changes to patients is not necessarily clear. This article reviews conceptual and methodological issues involved in determining the value of health effects in the context of economic evaluations of epilepsy therapies. Techniques for eliciting preferences for health effects are reviewed. The limited information on preferences for epilepsy-related health states is described. Directions for further research are suggested.
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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 | 0.175 | 0.455 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".