A method for defining a journal subset for a clinical discipline using the bibliographies of systematic reviews.
Bibliographic record
Abstract
BACKGROUND: Searching for best evidence for clinical decisions in large biomedical databases is problematic because advances in health care practice that are ready for application are but a very dilute constituent in a much larger pool of biomedical literature. Sensitive search strategies have been developed to help alleviate this problem but search precision is still generally low. If "virtual journal subsets" that are likely to include all relevant articles can be defined for clinical discipline areas or disease content areas this will likely improve search precision. OBJECTIVE: To determine whether studies cited in systematic literature reviews can define a journal subset for a given clinical discipline. DESIGN: Survey of the primary studies included in systematic reviews that are relevant to the clinical discipline of nephrology. METHODS: Four data sources were searched to identify systematic reviews relevant to clinical nephrology: the Cochrane Database of Systematic Reviews, McMaster PLUS (Premium LiteratUre Service), MEDLINE, and the Renal Health Library. Three research assistants recorded data pertinent to each of the included primary studies. RESULTS: 195 systematic reviews relevant to nephrology were defined and the 2,779 unique original articles they cited were concentrated in 466 journals, with 90% of the articles in 217 titles. This journal subset can be stored online and used when searching the large biomedical data-bases such as MEDLINE. CONCLUSION: The bibliographies of systematic reviews can be used to define a journal subset for a clinical discipline area.
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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 | 0.156 | 0.473 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.082 | 0.050 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".