A bibliometric analysis of the 100 most-influential papers in the field of anti-diabetic drugs
Bibliographic record
Abstract
Aim: We analyzed the 100 most-cited articles on all anti-diabetic drugs. A comprehensive literature review found no bibliometrics on this. Methods: Two researchers independently extracted articles from Scopus and ranked them by citation count as the ‘top 100 most-cited’. Results: The median number of citations is 1385.5. Most articles are from the USA (n = 59). Insulin has the most papers (n = 24). Majority (n = 76) were privately funded and contained at least one conflict of interest (n = 66). The New England Journal of Medicine has the most publications (n = 44). Male authors made majority of both first and last authorship positions. Conclusion: This study aims to aid in directing future research and in reducing biases. The 100 most cited anti-diabetic original articles were published between 1971–2022 from a total of 46 nations. The highest frequency of articles occurred between 2006–2010 (n = 27). The median number of citations was 1385.5, ranging from 774 to 22,496. Authors from 46 nations contributed to this list; however, more than half of these articles were from the USA (n = 59), followed by the UK (n = 31) and Canada (n = 24). Insulin had the most papers published (n = 24). The New England Journal of Medicine (n = 44) and The Lancet (n = 18) have contributed most to the publications. The analysis also highlighted a gender disparity in first and senior authorship positions, with male authors predominating in anti-diabetic research. Most articles (n = 76) were funded privately, followed by publicly funded (n = 49). The average number of authors with a conflict of interest was 5.23, and 66/100 publications contained at least one.
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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 | 0.014 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.224 | 0.210 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".