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Record W3159048145 · doi:10.3899/jrheum.210164

Authorship Trends in the Rheumatology Literature, 1990–2020

2021· letter· en· W3159048145 on OpenAlexvenueno aff
Ikechukwu Mbonu, Iryna Nemesh, Elizabeth Suelzer, Abigail Thorgerson, Michael Putman

Bibliographic record

VenueThe Journal of Rheumatology · 2021
Typeletter
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatologyInternal medicineMEDLINEFamily medicine

Abstract

fetched live from OpenAlex

To the Editor: The number of cited authors per publication has increased substantially over time in the field of medicine.1,2 This trend of “authorship inflation” has been observed in high-impact medical journals1 and multiple medical subspecialties,2 but to our knowledge has yet to be assessed in the field of rheumatology. In this analysis, we describe the number of cited authors in rheumatology publications over a 30-year period. A search of PubMed (NLM) MEDLINE-indexed rheumatology publications from January 1, 1990, to January 11, 2020, was designed by an experienced research librarian and conducted to identify rheumatology clinical practice guidelines, randomized controlled trials (RCTs), systematic reviews and metaanalyses (SRMAs), non-SRMA review articles, and case reports (see Supplementary Data for search terms and strategy, …

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

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.101
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.101
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0210.033
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.034
GPT teacher head0.298
Teacher spread0.264 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainReporting
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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