Indexing of diagnosis accuracy studies in MEDLINE and EMBASE.
Bibliographic record
Abstract
BACKGROUND: STAndards for Reporting of Diagnostic Accuracy (STARD) were published in 2003 and endorsed by some journals but not others. OBJECTIVE: To determine whether the quality of indexing of diagnostic accuracy studies in MEDLINE and EMBASE has improved since the STARD statement was published. DESIGN: Evaluate the change in the mean number of "accurate index terms" assigned to diagnostic accuracy studies, comparing STARD (endorsing) and non-STARD (non-endorsing) journals, for 2 years before and after STARD publication. RESULTS: In MEDLINE, no differences in indexing quality were found for STARD and non-STARD journals before or after the STARD statement was published in 2003. In EMBASE, indexing in STARD journals improved compared with non-STARD journals (p = 0.02). However, articles in STARD journals had half the number of accurate indexing terms as articles in non-STARD journals, both before and after STARD statement publication (p < 0.001).
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 | 0.074 | 0.373 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.104 | 0.100 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".