Developing a cross-validation tool for evaluating economic evidence in rapid literature reviews
Bibliographic record
Abstract
Background: Rapid economic reviews efficiently summarize economic evidence. However, reporting main findings without assessing quality and credibility can be misleading. The objective of this study was to develop a rapid cross-validation screening tool to evaluate economic evidence when conducting rapid economic literature reviews. Methods: This article outlines our reasoning and the theoretical concepts for developing the screening tool. Results: This cross-validation tool is a qualitative approach under a Bayesian framework that uses prior health economic evidence to gauge the credibility of the rapid economic review's findings. This article describes an application of this tool and highlights practical considerations for its development and deployment. Conclusion: This tool can provide a valuable screening instrument to evaluate the quality and credibility of the economic evidence.
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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 | 0.598 | 0.853 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.029 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".