Comparison of search strategies in systematic reviews of adverse effects to other systematic reviews
Bibliographic record
Abstract
BACKGROUND: Research indicates that the methods used to identify data for systematic reviews of adverse effects may need to differ from other systematic reviews. OBJECTIVES: To compare search methods in systematic reviews of adverse effects with other reviews. METHODS: The search methodologies in 849 systematic reviews of adverse effects were compared with other reviews. RESULTS: Poor reporting of search strategies is apparent in both systematic reviews of adverse effects and other types of systematic reviews. Systematic reviews of adverse effects are less likely to restrict their searches to MEDLINE or include only randomised controlled trials (RCTs). The use of other databases is largely dependent on the topic area and the year the review was conducted, with more databases searched in more recent reviews. Adverse effects search terms are used by 72% of reviews and despite recommendations only two reviews report using floating subheadings. CONCLUSIONS: The poor reporting of search strategies in systematic reviews is universal, as is the dominance of searching MEDLINE. However, reviews of adverse effects are more likely to include a range of study designs (not just RCTs) and search beyond MEDLINE.
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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 | 0.392 | 0.749 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.028 | 0.041 |
| Bibliometrics | 0.070 | 0.061 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".