Response to Corrao et al.: Improving Efficacy of PubMed Clinical Queries for Retrieving Scientifically Strong Studies on Treatment
Bibliographic record
Abstract
A recent study by Corrao et al.1 assessed the retrieval power of the narrow (specific) search strategy for therapy available on the Clinical Queries screen in PubMed (http://www.ncbi.nlm.nih.gov/entrez/query/static/clinical.shtml) which was developed by our research group at McMaster University.2 They compared its retrieval power with a modified search string that included the Britannic English term “randomised”. We welcome such an analysis of our work and encourage researchers to continue to investigate improved ways of searching in MEDLINE. Corrao et al. stated that the narrow therapy search strategy available on Clinical Queries, (randomized controlled trial[Publication Type] OR (randomized[Title/Abstract] AND controlled[Title/Abstract] AND trial[Title/Abstract])) , may introduce bias because it may cut off studies about therapy in which “randomized” was exclusively written as “randomised”. Thus, they modified the search string to …
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 | 0.025 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.049 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".