Retrieving randomized controlled trials from <scp>medline</scp>: a comparison of 38 published search filters
Bibliographic record
Abstract
BACKGROUND: People search medline for trials of healthcare interventions for clinical decisions, or to produce systematic reviews, practice guidelines, or technology assessments. Finding all relevant randomized controlled trials (RCTs) with little extraneous material is challenging. OBJECTIVE: To provide comparative data on the operating characteristics of search filters designed to retrieve RCTs from medline. METHODS: We identified 38 filters. The testing database comprises handsearching data from 161 clinical journals indexed in medline. Sensitivity, specificity and precision were calculated. RESULTS: The number of terms and operating characteristics varied considerably. Comparing the retrieval against the single term 'randomized controlled trials.pt.' (sensitivity for retrieving RCTs, 93.7%), 24 of 38 filters had statistically higher sensitivity; 6 had a sensitivity of at least 99.0%. Four other filters had specificities (non retrieval of non-RCTs) that were statistically not different or better than the single term (97.6%). Precision was poor: only two filters had precision (proportion of retrieved articles that were RCTs) statistically similar to that of the single term (56.4%)-all others were lower. Filters with more search terms often had lower specificity, especially at high sensitivities. CONCLUSION: Many RCT filters exist (n = 38). These comparative data can direct the choice of an RCT filter.
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.
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 | 0.393 | 0.786 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.044 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".