Can database-level MEDLINE exclusion filters in Embase and CINAHL be used to remove duplicate records without loss of relevant studies in systematic reviews? An exploratory study
Bibliographic record
Abstract
Objective: To investigate whether using database filters to remove MEDLINE results within Embase (OVID) and CINAHL (EBSCO) would result in fewer records, without leading to any loss of studies included in the final review. Methods: We reviewed the included studies from a sample set of 20 Cochrane Reviews, and replicated the search strategies from those reviews in MEDLINE, Embase (both on the OVID platform) and CINAHL (EBSCO). Results were exported to EndNote; then relevant MEDLINE filters were applied within each database, and results were exported again. Filtered results were analysed to determine whether the filtered Embase and CINAHL results excluded relevant studies that were not identified in the original MEDLINE search. Results: Using the “Records from: Embase” filter resulted in no loss of included studies; however, the “Exclude MEDLINE journals” filter in Embase resulted in a failure to retrieve a large number of relevant studies. CINAHL’s filter for MEDLINE records resulted in a very small number of studies being lost. Conclusions: The “Records from: Embase” filter may be safely used for deduplication, though as it removes conferences, searchers may also want to review Conference abstracts separately using the Conferences filter. CINAHL’s MEDLINE filter comes with a small risk of filtering out relevant studies, but may be appropriate to use. Though we did not set out to address this question, our results also demonstrate that it is not advisable to rely on an unfiltered search of Embase alone in order to identify all relevant studies.
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 | 0.665 | 0.897 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.028 | 0.042 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".