PubMed perspective of family medicine research: where does it stand?
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to obtain a view of family medicine research by analyzing PubMed citations from 1960-2003. METHOD: Family practice (FP) citations in PubMed from 1960 to 2003 were downloaded in MEDLINE format. This was written into relation database using 'PubMed Grabber/Analyzer' software developed at University of Kelaniya, Sri Lanka. Search Query Language (SQL) and online PubMed queries were used for further analysis. RESULTS: There were 50288 FP citations from 80 countries. Of these, 33712 (67%) citations were from 15 FP journals. United Kingdom (18760), United States (13584), Australia (3262), Canada (1848), Germany-west (1340) were the five countries which had the most citations and 22 countries had less than 5 citations. Van Weel C (118), Geyman JP (116), Olesen F (87), Jones R (83) and Knottnerus JA (82) were numerically, the top five authors. Only 921 authors had more than 10 citations and the vast majority of authors had only one citation. Letters (5121), review (2715), editorial (2259), randomized controlled trials-RCT (1585) and Meta-analysis (44) were the top publication types. 40 citations found under 'qualitative research'. Discussion. The relatively few PubMed FP citations (50288) are by a small number of academics in developed countries. Citations showed an upsurge from the mid 1980s to the late 1990's but reached a plateau in the new millennium. Compared to PubMed citations from 1960-2003 in other specialties such as 2737655 for public health, 1151194 for cardiology & cardiovascular diseases and 318538 for medical informatics, the 50288 FP citations were paltry. Paucity of RCT (1585) and meta-analysis (44) was noted. The low 'qualitative research' citations (44) could have been due to the late introduction of the MeSH concept in 2003. CONCLUSIONS: Priority should be given to increase FP research and also to ensure the indexing of FP journals that are not currently indexed in PubMed. Efforts to increase citations in Medline may not give the desired results because of low priority given primary care specialties such as family medicine in the USA. Alternative solution of a separate bibliographic database for FP similar to PsycInfo may be too costly.
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 | 0.038 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.032 | 0.035 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".