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Record W4243675391 · doi:10.1016/j.joca.2015.03.003

OARSI Clinical Trials Recommendations: Hand imaging in clinical trials in osteoarthritis

2015· review· en· W4243675391 on OpenAlexaff
David J. Hunter, Nigel Arden, Flavia Cicuttini, M.D. Crema, Bernard J. Dardzinski, J. Duryea, Ali Guermazi, I.K. Haugen, M. Kloppenburg, E. Maheu, Colin G. Miller, Johanne Martel‐Pelletier, R. Elena Ochoa‐Albiztegui, Jean‐Pierre Pelletier, C. Peterfy, Frank W. Roemer, Garry E. Gold

Bibliographic record

VenueOsteoarthritis and Cartilage · 2015
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversité de Montréal
FundersNational Health and Medical Research CouncilNational Institutes of HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesBioClinica
KeywordsClinical trialMedical physicsMedicineProtocol (science)Reliability (semiconductor)Quality assuranceOsteoarthritisPhysical therapyAlternative 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

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)0.117
metaresearch head score (Gemma)0.182
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.117
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1170.182
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0160.014
Bibliometrics0.0080.008
Science and technology studies0.0020.004
Scholarly communication0.0090.005
Open science0.0080.005
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0240.008

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 head0.249
GPT teacher head0.492
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations28
Published2015
Admission routes1
Has abstractno

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