Bibliographic record
Abstract
PURPOSE: To identify and examine the characteristics of the 50 top-cited articles in medical education. METHOD: Two searches were conducted in the Web of Knowledge database in March 2014: a search of medical education journals in the category "Education, Scientific Discipline" (List A) and a keyword search across all journals (List B). Articles identified were reviewed for citation count, country of origin, article type, journal, authors, and publication year. RESULTS: Both lists included 56 articles, not 50, because articles with the same absolute number of citations shared the same rank. The majority of List A articles were published in Academic Medicine (34; 60.7%) and Medical Education (16; 28.6%). In List B, 27 articles (48.2%) were published in medical education journals, 19 (33.9%) in general medicine and surgery journals, and 10 (17.9%) in higher education and educational psychology journals. Twenty-six articles were included in both lists, with different rankings. Reviews and articles constituted the majority of articles; there were only 8 research papers in List A and 13 in List B. Articles mainly originated from the United States, Canada, the Netherlands, and the United Kingdom. The majority were published from 1979 to 2007. There was no correlation between year and citation count. CONCLUSIONS: The finding that over half of List B articles were published in nonmedical education journals is consistent with medical education's integrated nature and subspecialty breadth. Twenty of these articles were among their respective non-medical-education journals' 50 top-cited papers, showing that medical education articles can compete with subject-based articles.
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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.060 | 0.063 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".