The development of search filters for adverse effects of medical devices in <scp>medline</scp> and <scp>embase</scp>
Bibliographic record
Abstract
BACKGROUND: Objectively derived search filters for adverse drug effects and complications in surgery have been developed but not for medical device adverse effects. OBJECTIVE: To develop and validate search filters to retrieve evidence on medical device adverse effects from ovid medline and embase. METHODS: We identified systematic reviews from Epistemonikos and the Health Technology Assessment (hta) database. Included studies within these reviews that reported on medical device adverse effects were randomly divided into three test sets and one validation set of records. Using word frequency analysis from one test set, we constructed a sensitivity maximising search strategy. This strategy was refined using two other test sets, then validated. RESULTS: From 186 systematic reviews which met our inclusion criteria, 1984 unique included studies were available from medline and 1986 from embase. Generic adverse effects searches in medline and embase achieved 84% and 83% sensitivity. Recall was improved to over 90%, however, when specific adverse effects terms were added. CONCLUSION: We have derived and validated novel search filters that retrieve over 80% of records with medical device adverse effects data in medline and embase. The addition of specific adverse effects terms is required to achieve higher levels of sensitivity.
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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 | 0.324 | 0.654 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.043 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".