P.002 Improving the quality of systematic reviews of neurological conditions with more accurate search strategies: a series of validation studies
Bibliographic record
Abstract
Background: A well-constructed search strategy is an important feature of any systematic review. We aimed to design and validate electronic database (e.g. Pubmed) search strategies (i.e. a hedge or series of words used to identify articles of interest) for six neurological conditions. Methods: We enumerated 10311 consecutive articles in the 21 highest impact factor English-language general neurology journals. We constructed a simple hedge, limited to one keyword, for each condition. We also constructed a complex hedge using a series of MeSH terms and keywords. Two reviewers independently reviewed (confirmed by a third reviewer) all articles and established which condition(s) were the article’s subject. We calculated sensitivity/specificity estimates for the simple and complex hedges, and compared these using McNemar’s test. Results: The results are summarized in the Table. Conclusions: Our complex hedges for most conditions dramatically improve sensitivity without compromising specificity. This study will help improve the accuracy of search strategies in future systematic reviews.
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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 | 0.279 | 0.669 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".