Optimal search strategies for detecting health services research studies in MEDLINE
Bibliographic record
Abstract
BACKGROUND: Evidence from health services research (HSR) is currently thinly spread through many journals, making it difficult for health services researchers, managers and policy-makers to find research on clinical practice guidelines and the appropriateness, process, outcomes, cost and economics of health care services. We undertook to develop and test search terms to retrieve from the MEDLINE database HSR articles meeting minimum quality standards. METHODS: The retrieval performance of 7445 methodologic search terms and phrases in MEDLINE (the test) were compared with a hand search of the literature (the gold standard) for each issue of 68 journal titles for the year 2000 (a total of 25,936 articles). We determined sensitivity, specificity and precision (the positive predictive value) of the MEDLINE search strategies. RESULTS: A majority of the articles that were classified as outcome assessment, but fewer than half of those in the other categories, were considered methodologically acceptable (no methodologic criteria were applied for cost studies). Combining individual search terms to maximize sensitivity, while keeping specificity at 50% or more, led to sensitivities in the range of 88.1% to 100% for several categories (specificities ranged from 52.9% to 97.4%). When terms were combined to maximize specificity while keeping sensitivity at 50% or more, specificities of 88.8% to 99.8% were achieved. When terms were combined to maximize sensitivity and specificity while minimizing the differences between the 2 measurements, most strategies for HSR categories achieved sensitivity and specificity of at least 80%. INTERPRETATION: Sensitive and specific search strategies were validated for retrieval of HSR literature from MEDLINE. These strategies have been made available for public use by the US National Library of Medicine at www.nlm.nih.gov/nichsr/hedges/search.html.
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 | 0.155 | 0.590 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.115 | 0.066 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".