Evolving educational landscape in pathology: a comprehensive bibliometric and visual analysis including digital teaching and learning resources
Bibliographic record
Abstract
Aims Pathology education is a core component of medical training, and its literature is critical for refining educational modalities. We performed a cross-sectional bibliometric analysis to explore publications on pathology education, focusing on new medical education technologies. Methods The analysis identified 64 pathology journals and 53 keywords. Relevant articles were collected using a web application, PaperScraper, developed to accelerate literature search. Citation data were collected from multiple sources. Descriptive statistics, with time period analysis, were performed using Microsoft Excel and visualised with Flourish Studio. Two article groups were further investigated with a bibliometric software, VOSViewer, to establish co-authorship and keyword relationships. Results 8946 citations were retrieved from 905 selected articles. Most articles were published in the last decade (447, 49.4%). The top journals were Archives of Pathology & Laboratory Medicine (184), Human Pathology (122) and the American Journal of Clinical Pathology (117). The highest number of citations was found for Human Pathology (2120), followed by Archives of Pathology & Laboratory Medicine (2098) and American Journal of Clinical Pathology (1142). Authors with different backgrounds had the greatest number of articles and citations. 12 co-authorship, 3 keyword and 8 co-citation clusters were found for the social media/online resources group, 8 co-authorship, 4 keyword and 7 co-citation clusters for the digital pathology/virtual microscopy/mobile technologies group. Conclusions The analysis revealed a significant increase in publications over time. The emergence of digital teaching and learning resources played a major role in this growth. Overall, these findings underscore the transformative potential of technology in pathology education.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.138 | 0.148 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".