Developing optimal search strategies for detecting clinically sound causation studies in MEDLINE.
Bibliographic record
Abstract
BACKGROUND: Clinical end users of MEDLINE must be able to retrieve articles that are both scientifically sound and directly relevant to clinical practice. The use of methodologic search filters has been advocated to improve the accuracy of searching for such studies. These filters are available for the literature on therapy and diagnosis, but strategies for the literature on causation have been less well studied. OBJECTIVE: To determine the retrieval characteristics of methodologic terms in MEDLINE for identifying methodologically sound studies on causation. DESIGN: Comparison of methodologic search terms and phrases for the retrieval of citations in MEDLINE with a manual hand search of the literature (the gold standard) for 162 core health care journals. METHODS: 6 trained, experienced research assistants read all issues of 162 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 review articles. Search strategies were developed for all categories including causation. MAIN OUTCOME MEASURES: Sensitivity, specificity, precision, and accuracy of the search strategies. RESULTS: 12% of studies classified as causation met basic criteria for scientific merit for testing clinical applications. Combinations of terms reached peak sensitivities of 93%. Compared with the best single term, multiple terms increased sensitivity for sound studies by 15.5% (absolute increase), but with some loss of specificity when sensitivity was maximized. Combining terms to optimize sensitivity and specificity achieved sensitivities and specificities both above 80%. CONCLUSION: The retrieval of causation studies cited in MEDLINE can be substantially enhanced by selected combinations of indexing terms and textwords.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.193 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".