Updated generic search filters for finding studies of adverse drug effects in Ovid <scp>medline</scp> and Embase may retrieve up to 90% of relevant studies
Bibliographic record
Abstract
BACKGROUND: The most current objectively derived search filters for adverse drug effects are 15 years old and other strategies have not been developed and tested empirically. OBJECTIVE: To develop and validate search filters to retrieve evidence on adverse drug effects from Ovid medline and Ovid Embase. METHODS: We identified systematic reviews of adverse drug effects in Epistemonikos. From these reviews, we collated their included studies which we then randomly divided into three tests and one validation set of records. We constructed a search strategy to maximise relative recall using word frequency analysis with test set one. This search strategy was then refined using test sets two and three and validated on the final set of records. RESULTS: Of 107 systematic reviews which met our inclusion criteria, 1948 unique included studies were available from medline and 1980 from Embase. Generic adverse drug effects searches in medline and Embase achieved 90% and 89% relative recall, respectively. When specific adverse effects terms were added recall was improved. CONCLUSION: We have derived and validated search filters that retrieve around 90% of records with adverse drug effects data in medline and Embase. The addition of specific adverse effects terms is required to achieve higher recall.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".