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.
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
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 | Observational | 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 | 0.014 | 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, 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".