MétaCan
Menu
Back to cohort
Record W4367368093 · doi:10.1016/j.eururo.2023.04.017

Industry Payments to American Editorial Board Members of Major Urology Journals

2023· letter· en· W4367368093 on OpenAlexaff
David‐Dan Nguyen, Liam Murad, Anne Xuan-Lan Nguyen, Anudari Zorigtbaatar, David Bouhadana, Claudia Deyirmendjian, Kevin C. Zorn, Dean Elterman, Bilal Chughtai, Rashid K. Sayyid, Muhieddine Labban, Quoc‐Dien Trinh, Christopher J.D. Wallis, Naeem Bhojani

Bibliographic record

VenueEuropean Urology · 2023
Typeletter
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsMount Sinai HospitalCentre Hospitalier de l’Université de MontréalUniversité de MontréalMcGill University Health CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineUrologyEditorial boardFamily medicineLibrary science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.007
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0110.002
Scholarly communication0.0110.003
Open science0.0030.002
Research integrity0.0890.030
Insufficient payload (model declined to judge)0.0410.026

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 head0.317
GPT teacher head0.520
Teacher spread0.203 · 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
DomainIncentives
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

Citations11
Published2023
Admission routes1
Has abstractno

Explore more

Same venueEuropean UrologySame topicPharmaceutical industry and healthcareFrench-language works237,207