Inquisitio validus Index Medicus: A simple method of validating MEDLINE systematic review searches
Bibliographic record
Abstract
UNLABELLED: We offer a new method of validation for the effectiveness of MEDLINE searches used in systematic reviews, the Inquisitio Validus Index Medicus. Validation is essential to ensure that relevant studies are not missed by the MEDLINE search strategy. METHODS: To demonstrate the validation method, a sample of six updated Cochrane reviews with comprehensive searches was used. The MEDLINE searches of both the original and updated reviews were tested to determine the percent of eligible MEDLINE-indexed studies retrieved by the search (recall). RESULTS: The validation method was robust and was able to demonstrate that the retrieval of relevant studies from MEDLINE was sub-optimal. The approach to revising searches in our sample appeared unsystematic. Some poorly performing searches were used unchanged in the updates, and of the two amended strategies, one performed worse than the original when tested against studies included in the original, while the other improved recall. CONCLUSION: There is a clear need for search validation. Using this validation method can determine whether the search of the main database performs adequately or needs to be revised to improve recall, allowing the searcher an opportunity to improve their search strategy. Validation of the search is recommended for systematic reviews, where the intent is to identify all relevant studies. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.570 | 0.894 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.050 | 0.034 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".