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AB1678 CONSTRUCTION OF A COMPOSITE SCORE FOR PATIENT SELF-REPORT OF FLARE IN OSTEOARTHRITIS: A COMPARISON OF METHODS WITH THE FLARE-OA-16 QUESTIONNAIRE

2023· article· en· W4379652938 on OpenAlexafffund
Fabiana Queiroga, Jonathan Epstein, M.-L. Erpelding, Lauren King, Marc Soudant, Élisabeth Spitz, J.F. Maillefert, Bruno Fautrel, Leigh F. Callahan, Lyn March, David J. Hunter, Françis Guillemin

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of Toronto
FundersSociedad Española de ReumatologíaUniversity of TorontoSorbonne UniversitéLunds UniversitetAssistance Publique - Hôpitaux de ParisFundación Española de ReumatologíaAssistance publique-Hôpitaux de ParisAgence Nationale de la RechercheUniversité de Lorraine
KeywordsFlareMedicineOsteoarthritisPhysical therapyPathologyAlternative medicine

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.354
Teacher spread0.317 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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
Published2023
Admission routes2
Has abstractno

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Same venueAnnals of the Rheumatic Diseases→Same topicOsteoarthritis Treatment and Mechanisms→French-language works237,207→