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Gender representation in authorship in later-phase systemic clinical trials in biliary tract cancer (BTC).

2021· article· en· W3123016286 on OpenAlexaff
Mairéad G. McNamara, John Bridgewater, Lipika Goyal, David Goldstein, Rachna T. Shroff, Markus Moehler, Maeve A. Lowery, Tanios Bekaii‐Saab, Robin Kate Kelley, Junji Furuse, Lorenza Rimassa, Chigusa Morizane, Ángela Lamarca, Richard Hubner, Jennifer J. Knox, Juan W. Valle

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineClinical trialBiliary tract cancerGallbladder cancerSystemic therapyClinical endpointOncologyCancerClinical OncologyFamily medicineSurgeryGemcitabineBreast cancer

Abstract

fetched live from OpenAlex

348 Background: The proportion of females in medicine is increasing (approx. 50% in medical school/workforce), but disparities in female authorship in oncology research publications exist; female corresponding authorship reportedly ranges from 7.2-39.1% in oncology clinical trials (Ludmir et al 2019). This study aimed to describe and assess factors associated with female first and senior authorship in later phase systemic clinical trials in BTC and to identify any changes over time. Methods: Embase/Medline were used to identify final primary trial publications in BTC (2000-2020) (excluding phase I (PI) (expected to move to later phase), mixed tumour site trials, reviews, editorials and trial-in-progress publications). Gender was determined by inspection of names, google search and author communication. Chi-square tests and log regression were used to assess factors associated with female first and senior authorship, including changes over time (STATA16). Results: Of 501 publications, 163 met inclusion criteria; 80% single-arm PII and 15% and 5% randomised PII and PIII respectively; 73% enrolled ≤50 patients. Tumour primary sites were all BTC: 86%, cholangiocarcinoma: 8%, gallbladder cancer: 6%; 80% involved chemotherapy, 13% targeted therapy and 5% localised/systemic combinations; 65% were in first-line (1L) advanced setting, 17% post 1L, 13% advanced non-specified and 5% neo-adjuvant/adjuvant. Forty-eight percent received industry funding and 65% met primary end-point. Sixty-four percent were published post ABC-02 (Valle et al 2010). Publication impact factor (IF) was ≤5 in 50% and >20 in 12%. Median number of authors in all publications was 11. Geographic location of all first and senior authors were Asia (42%/42%), Europe (29%/29%), USA (24%/22%) and other (4%/6%), respectively. Median individual trial female author representation was 25%; there were no female authors in 12% of trials. Overall, female first and senior author representation was 21% and 11%, respectively. Median position of first female author was second. In publications with IF ≤20 and >20, there were 22% and 16% female first and 13% and 0% female senior authors, respectively. The phase of trial, journal IF, industry funding, or whether met primary end-point did not impact female first or senior author representation (all P>.05). There were more female senior authors associated with “other” geographic locations (40% in 10 trials) (P=.016) vs Asia (7%), Europe (8%) and USA (14%). There were no significant changes in female first or senior author representation over time (‘00-05: 21%/18%, ‘06-10: 27%/5%, ‘11-15: 15%/15%, ‘16-20: 22%/9%, P=.738, and P=.508 respectively). Conclusions: Female first and senior author representation in later phase systemic clinical trial publications in BTC is low and has not changed significantly over time. The underlying reasons for this imbalance need to be better understood and addressed.

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)0.038
metaresearch head score (Gemma)0.163
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.163
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.589
GPT teacher head0.573
Teacher spread0.015 · 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
DomainEvaluation
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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