Filtering failure: the impact of automated indexing in Medline on retrieval of human studies for knowledge synthesis
Bibliographic record
Abstract
Objective: Use of the search filter 'exp animals/not humans.sh' is a well-established method in evidence synthesis to exclude non-human studies. However, the shift to automated indexing of Medline records has raised concerns about the use of subject-heading-based search techniques. We sought to determine how often this string inappropriately excludes human studies among automated as compared to manually indexed records in Ovid Medline. Methods: We searched Ovid Medline for studies published in 2021 and 2022 using the Cochrane Highly Sensitive Search Strategy for randomized trials. We identified all results excluded by the non-human-studies filter. Records were divided into sets based on indexing method: automated, curated, or manual. Each set was screened to identify human studies. Results: Human studies were incorrectly excluded in all three conditions, but automated indexing inappropriately excluded human studies at nearly double the rate as manual indexing. In looking specifically at human clinical randomized controlled trials (RCTs), the rate of inappropriate exclusion of automated-indexing records was seven times that of manually-indexed records. Conclusions: Given our findings, searchers are advised to carefully review the effect of the 'exp animals/not humans.sh' search filter on their search results, pending improvements to the automated indexing process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".