Comparison of search strategies in systematic reviews of adverse effects to other systematic reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.235 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it