Development and validation of a geographic search filter for MEDLINE (PubMed) to identify studies conducted in Germany
Bibliographic record
Abstract
Validated geographic search filters are scarce, with none previously available for Germany.Our aim was to develop and validate a highly sensitive geographic search filter for MEDLINE (PubMed) that identifies studies conducted in Germany.First, using the relative recall method, we created a gold standard set of German studies, dividing it into 'development' and 'testing' sets.Next, candidate search terms were identified using (i) term frequency analyses in the 'development set' and a random set of MEDLINE records; and (ii) a list of German geographic locations, compiled by our team.Then, we iteratively created the filter, evaluating it against the 'development' and 'testing' sets.To validate the filter, we conducted case studies (CSs) and a simulation study, using a sample of systematic reviews (SRs) that included studies conducted in Germany without geographically restricting their search strategy.When applying the filter to the original search strategies of the 17 SRs eligible for CSs, the median precision was 2.64% (interquartile range [IQR]: 1.34-6.88%)vs. 0.16% (IQR: 0.10-0.49%)without the filter.The median number-needed-to-read (NNR) decreased from 625 (IQR: 211-1042) to 38 (IQR: 15-76).The filter achieved 100% sensitivity in 13 CSs, 85.71% in 2 CSs, and 87.50% and 80% in the remaining 2 CSs.In a simulation study, the filter demonstrated an overall sensitivity of 97.19% and NNR of 42.The validation results indicate that our filter reliably identifies studies conducted in Germany, enhancing screening process efficiency and can be applied in SRs and other evidence syntheses aiming to identify German studies.
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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 | 0.042 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.041 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".