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Physicians’ vs patients’ global assessments of disease activity in rheumatology and musculoskeletal trials: A meta-research project with focus on reasons for discrepancies

2022· review· en· W4286267560 on OpenAlexaff
Christian Lynæs, Maria Lynæs, Lee S. Simon, Peter Tugwell, Maria Antonietta D’Agostino, Vibeke Strand, Carsten Bogh Juhl, Sabrina Mai Nielsen, Maarten De Witt, Dorcas Beaton, Lara Maxwell, Alexa Meara, Robin Christensen

Bibliographic record

VenueSeminars in Arthritis and Rheumatism · 2022
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsOttawa HospitalInstitute for Work & HealthUniversity of Ottawa
FundersParker Institute for Cancer ImmunotherapyAgence Nationale de la RechercheOak Foundation
KeywordsMedicineRheumatologyInternal medicinePhysical therapyMusculoskeletal diseaseMeta-analysisClinical trialAlternative medicineDiseaseFamily medicinePathology

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

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
gemmaMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.173
metaresearch head score (Gemma)0.291
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.827
Threshold uncertainty score0.916

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1730.291
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0190.064
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0070.004
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.113
GPT teacher head0.440
Teacher spread0.327 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Systematic review
DomainMethods
GenreReview

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

Citations6
Published2022
Admission routes1
Has abstractno

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