Increasing number of databases searched in systematic reviews and meta-analyses between 1994 and 2014
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine whether the number of bibliographic databases used to search the health sciences literature in individual systematic reviews (SRs) and meta-analyses (MAs) changed over a twenty-year period related to the official 1995 launch of the Cochrane Database of Systematic Reviews (CDSR). METHODS: Ovid MEDLINE was searched using a modified version of a strategy developed by the Scottish Intercollegiate Guidelines Network to identify SRs and MAs. Records from 3 milestone years were searched: the year immediately preceding (1994) and 1 (2004) and 2 (2014) decades following the CDSR launch. Records were sorted with randomization software. Abstracts or full texts of the records were examined to identify database usage until 100 relevant records were identified from each of the 3 years. RESULTS: The mean and median number of bibliographic databases searched in 1994, 2004, and 2014 were 1.62 and 1, 3.34 and 3, and 3.73 and 4, respectively. Studies that searched only 1 database decreased over the 3 milestone years (60% in 1994, 28% in 2004, and 10% in 2014). CONCLUSIONS: The number of bibliographic databases searched in individual SRs and MAs increased from 1994 to 2014.
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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 | 0.072 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.085 | 0.121 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".