The pond is wider than you think! Problems encountered when searching family practice literature.
Bibliographic record
Abstract
OBJECTIVE: To explain differences in the results of literature searches in British general practice and North American family practice or family medicine. DESIGN: Comparative literature search. SETTING: The Department of Family and Community Medicine at the University of Toronto in Ontario. METHOD: Literature searches on MEDLINE demonstrated that certain search strategies ignored certain key words, depending on the search engine and the search terms chosen. Literature searches using the key words "general practice," "family practice," and "family medicine" combined with the topics "depression" and then "otitis media" were conducted in MEDLINE using four different Web-based search engines: Ovid, HealthGate, PubMed, and Internet Grateful Med. MAIN OUTCOME MEASURES: The number of MEDLINE references retrieved for both topics when searched with each of the three key words, "general practice," "family practice," and "family medicine" using each of the four search engines. RESULTS: For each topic, each search yielded very different articles. Some search engines did a better job of matching the term "general practice" to the terms "family medicine" and "family practice," and thus improved retrieval. The problem of language use extends to the variable use of terminology and differences in spelling between British and American English. CONCLUSION: We need to heighten awareness of literature search problems and the potential for duplication of research effort when some of the literature is ignored, and to suggest ways to overcome the deficiencies of the various search engines.
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 | 0.331 | 0.712 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.028 | 0.032 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".