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Stakeholder endorsement advancing the implementation of a patient-reported domain for harms in rheumatology clinical trials: Outcome of the OMERACT Safety Working Group

2023· article· en· W4388197652 on OpenAlexaff
Dorthe B. Berthelsen, Lee S. Simon, John P. A. Ioannidis, Marieke Voshaar, P. Scott Richards, Niti Goel, Vibeke Strand, Sabrina Mai Nielsen, Beverly Shea, Peter Tugwell, Susan J. Bartlett, Glen Hazlewood, Lyn March, Jasvinder A. Singh, María E. Suarez‐Almazor, Maarten Boers, Randall M. Stevens, Daniel E. Furst, Thasia Woodworth, Amye Leong, Peter Brooks, Caroline Flurey, Robin Christensen

Bibliographic record

VenueSeminars in Arthritis and Rheumatism · 2023
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of CalgaryMcGill UniversityOttawa HospitalResearch CanadaUniversity of Ottawa
FundersSyddansk UniversitetOdense UniversitetshospitalParker Institute for Cancer Immunotherapy
KeywordsMedicineRheumatologyOutcome (game theory)Internal medicineStakeholderClinical trialFamily medicineAlternative medicinePhysical therapyPublic relationsPathology

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)0.739
metaresearch head score (Gemma)0.778
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.261
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7390.778
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0020.002
Science and technology studies0.0050.008
Scholarly communication0.0140.009
Open science0.0070.018
Research integrity0.0230.021
Insufficient payload (model declined to judge)0.0100.002

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 head0.083
GPT teacher head0.405
Teacher spread0.322 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations10
Published2023
Admission routes1
Has abstractno

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