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A bibliometric analysis of the 100 most-influential papers in the field of anti-diabetic drugs

2024· article· en· W6976740840 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement, Economics, and Public Policy
Canadian institutionsnot available
Fundersnot available
KeywordsBibliometricsScopusCitationCitation analysisWeb of scienceField (mathematics)Original research

Abstract

fetched live from OpenAlex

<b>Aim:</b> We analyzed the 100 most-cited articles on all anti-diabetic drugs. A comprehensive literature review found no bibliometrics on this. <b>Methods:</b> Two researchers independently extracted articles from Scopus and ranked them by citation count as the ‘top 100 most-cited’. <b>Results:</b> 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. <b>Conclusion:</b> 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). <i>The New England Journal of Medicine</i> (n = 44) and <i>The Lancet</i> (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.

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 armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0240.088
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0490.000

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.

Opus teacher head0.021
GPT teacher head0.265
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it