Age-Specific Search Strategies for Medline
Bibliographic record
Abstract
BACKGROUND: Many clinicians and researchers are interested in patients of a specific age (childhood, geriatrics, and so on). Searching for age-specific publications in large bibliographic databases such as Medline is problematic because of inconsistencies in indexing, overlapping age categories, and the spread of the relevant literature over many journals. To our knowledge, no empirically tested age-specific search strategies exist for Medline. OBJECTIVE: We sought to determine the retrieval characteristics of age-specific terms in Medline for identifying studies relevant for five clinical specialties: adult medicine, geriatric medicine, pediatric medicine, neonatal medicine, and obstetrics. METHODS: We compared age-specific search terms and phrases for the retrieval of citations in Medline with a manual hand search of the literature for 161 core health care journals. Six experienced research assistants who were trained and intensively calibrated read all issues of 161 journals for the publishing year 2000. In addition to classifying all articles for purpose and quality, study participants' ages were also recorded. Outcome measures were sensitivity, specificity, precision, and accuracy of single and combination search terms. RESULTS: When maximizing sensitivity, the best sensitivity and specificity achieved with combination terms were 98% and 81.2%, respectively, for pediatric medicine, 96.4% and 55.9% for geriatric medicine, 95.3% and 83.6% for neonatal medicine, 94.9% and 64.5% for adult medicine, and 82% and 97.1% for obstetrics. When specificity was maximized, all disciplines had an expected decrease in sensitivity and an increase in precision. Highest values for optimizing best sensitivity and specificity were achieved in neonatal medicine, 92.5% and 92.6%, respectively. CONCLUSION: Selected single terms and combinations of MeSH terms and textwords can reliably retrieve age-specific studies cited in Medline.
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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 | 0.038 | 0.194 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.077 | 0.062 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.017 |
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".