Intra-Specialty Citation Pattern in Radiology and Gastroenterology/Hepatology Journals: A Cross-Specialty Comparison
Bibliographic record
Abstract
Objectives: To investigate intra-specialty citation patterns of radiology articles, compared with another medical specialty: gastroenterology/hepatology. Methods: Four radiology journals ( Radiology , European Radiology , Diagnostic and Interventional Imaging , Canadian Association of Radiologists Journal ) and four gastroenterology/hepatology journals ( Journal of Hepatology, Journal of Gastroenterology, World Journal of Gastroenterology, Journal of Clinical Gastroenterology ) with similar Web of Science in-category 2020 IF ranking were selected. The original research, review, letter, and editorial articles published in these journals in 2021 were identified. The average number of intra-specialty citations per article (intra-specialty citation count ) and percentage of intra-specialty citations out of total citations per article (intra-specialty citation rate ) were compared between radiology and gastroenterology/hepatology articles using Student’s t -test. Results: The radiology articles demonstrated a lower total citation count per article (radiology: 29.7 ± .4 (mean ± SEM), n = 2063; gastroenterology/hepatology: 50.1 ± 1.4, n = 1335). The intra-specialty citation count was also lower in radiology articles than gastroenterology/hepatology articles (radiology: 12.9 ± .2, gastroenterology/hepatology: 19.6 ± .7; P < .001), both overall and in all article types. Additionally, the overall intra-specialty citation rate was not significantly different between the two specialties (radiology: 48.8% ± .5%; gastroenterology/hepatology: 47.1 ± .8%; P = .057), although the intra-specialty citation rates were higher in radiology original research and editorial article types. Conclusions: The significantly lower per-article intra-specialty citation counts in all radiology article types, a measurement that directly links to specialty IFs, may contribute to the lower impact factors of radiology journals compared with gastroenterology/hepatology ones.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".