MEDLINE clinical queries are robust when searching in recent publishing years
Bibliographic record
Abstract
OBJECTIVE: To determine if the PubMed and Ovid MEDLINE clinical queries (which were developed in the publishing year 2000, for the purpose categories therapy, diagnosis, prognosis, etiology, and clinical prediction guides) perform as well when searching in current publishing years. METHODS: A gold standard database of recently published research literature was created using the McMaster health knowledge refinery (http://hiru.mcmaster.ca/hiru/HIRU_McMaster_HKR.aspx) and its continuously updated database, McMaster PLUS (http://hiru.mcmaster.ca/hiru/HIRU_McMaster_PLUS_projects.aspx). This database contains articles from over 120 clinical journals that are tagged for meeting or not meeting criteria for scientific merit and clinical relevance. The clinical queries sensitive ('broad') and specific ('narrow') search filters were tested in this gold standard database, and sensitivity and specificity were calculated and compared with those originally reported for the clinical queries. RESULTS: In all cases, the sensitivity of the highly sensitive search filters and the specificity of the highly specific search filters did not differ substantively when comparing results derived in 2000 with those derived in a more current database. In addition, in all cases, the specificities for the highly sensitive search filters and the sensitivities for the highly specific search filters remained above 50% when testing them in the current database. DISCUSSION: These results are reassuring for modern-day searchers. The clinical queries that were derived in the year 2000 perform equally well a decade later. CONCLUSION: The PubMed and Ovid MEDLINE clinical queries have been revalidated and remain a useful public resource for searching the world's medical literature for research that is most relevant to clinical care.
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 | 0.190 | 0.716 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.064 | 0.069 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".