Robustness of empirical search strategies for clinical content in MEDLINE.
Bibliographic record
Abstract
BACKGROUND: It is important for clinical end users of MEDLINE to be able to retrieve articles that are both scientifically sound and directly relevant to clinical practice. The use of methodologic search filters (such as "random allocation" for sound studies of medical interventions) has been advocated to improve the accuracy of searching for such studies. Methodologic search filters have been tested in previous MEDLINE files but indexing continues to evolve and the operating characteristics of these search filters in current MEDLINE files are unknown. OBJECTIVE: To determine the robustness of empirical search strategies developed in 1991 for detecting clinical content in MEDLINE in the year 2000. DESIGN: A survey based on a hand search of 171 core health care journals using predetermined quality indicators for scientific merit and clinical relevance. METHODS: 6 trained, experienced research assistants read all issues of 171 journals for the publishing year 2000. Each article was rated using purpose and quality indicators and categorized into clinically relevant original studies, review articles, general papers, or case reports. The original and review articles were then categorized as 'pass' or 'fail' for methodologic rigor in the areas of therapy/quality improvement, diagnosis, prognosis, causation, economics, clinical prediction, and qualitative and review articles. Search strategies developed in 1991 were tested in the 2000 database. MAIN OUTCOME MEASURES: Sensitivity, specificity, precision, and accuracy of the search strategies. RESULTS: Search strategies developed in 1991 generally performed at least as well in 2000 for both best single terms and combinations of terms for high-sensitivity MEDLINE searches for studies of treatment, prognosis, etiology and diagnosis. For example, the accuracy of "clinical trial (pt)" rose from 91.6% to 94.4% (P<0.05) for retrieving high-quality studies of treatments. CONCLUSION: Most MEDLINE search strategies developed in 1991 are robust when searching in the publishing year 2000.
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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 | 0.643 | 0.941 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".