Bibliometric analysis of Iranian Authors in High-Impact Medical Science Journals
Bibliographic record
Abstract
This retrospective study aims to bibliometrically analyze Iranian publications in high-impact medical journals, considering the vital role of these journals in guiding scientific research. Articles by Iranian researchers in the top 10 medical specialty journals across 41 categories, published between 2013 and 2022, were extracted based on the Journal Citation Reports (JCR) in 2023. The publications of the top 5 Middle Eastern countries were also examined. A total of 3,737 articles were published by 35,650 authors (9.5 authors per paper). Teams wrote half of the articles of 3-6 members. The average number of citations per article was 37.43, and 58% of the articles had funding. Articles with funding received more citations than those without funding, and articles involving international collaboration received more citations than those involving national cooperation. Iran had the highest annual growth rate of publications among the top 5 Middle Eastern countries. Iranian authors were present in articles with international collaboration as first, corresponding, and last authors in 55.13%, 44.14%, and 32.15% of articles, respectively. Iranian researchers collaborated with 162 countries, with the USA (almost a quarter), England, and Canada as the main partners. China had the highest annual growth rate (26.7%) among major partners. Most publications were in nutrition and diet, tropical medicine, endocrinology, and metabolism. This study provides a comprehensive map for Iranian policymakers and researchers, as well as for policymakers in Middle Eastern countries, to inform the development of health research. It highlights the importance of international collaboration and funding.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.008 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.106 | 0.130 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".