Optimal search strategies for identifying sound clinical prediction studies in EMBASE
Bibliographic record
Abstract
BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges") for retrieval of empirically tested clinical prediction guides from EMBASE. METHODS: An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. RESULTS: 163 clinical prediction studies were identified, of which 69 (42.3%) passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1%) was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8%) was found in a 2-term strategy, but with a considerable fall in sensitivity to 60.9%. All single term strategies performed less well than 2- and 3-term strategies. CONCLUSION: The retrieval of sound clinical prediction studies from EMBASE is supported by several search strategies.
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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 | 0.094 | 0.424 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.067 | 0.044 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".