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Record W2604745577 · doi:10.1002/art.40060

2016 American College of Rheumatology/European League Against Rheumatism Criteria for Minimal, Moderate, and Major Clinical Response in Juvenile Dermatomyositis: An International Myositis Assessment and Clinical Studies Group/Paediatric Rheumatology International Trials Organisation Collaborative Initiative

2017· article· en· W2604745577 on OpenAlexaff
Lisa G. Rider, Rohit Aggarwal, Angela Pistorio, Nastaran Bayat, Brian Erman, Brian M. Feldman, Adam M. Huber, Rolando Cimaz, Rubén Cuttica, Sheila Knupp de Oliveira, Carol B. Lindsley, Clarissa Pilkington, Marilynn Punaro, Angelo Ravelli, Ann M. Reed, Kelly Rouster‐Stevens, Annet van Royen‐Kerkhof, Frank Dressler, Cláudia Saad Magalhães, Tamás Constantin, Joyce Davidson, Bo Magnusson, Ricardo Russo, Luca Villa, Mariangela Rinaldi, Howard E. Rockette, Peter A. Lachenbruch, Frederick W. Miller, Jiří Vencovský, Nicolino Ruperto

Bibliographic record

VenueArthritis & Rheumatology · 2017
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsIzaak Walton Killam Health CentreHospital for Sick Children
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthVersus ArthritisEuropean League Against RheumatismCure JM FoundationNational Institute of Environmental Health SciencesNational Institute for Health and Care ResearchMedical Research CouncilMyositis Association
KeywordsJuvenile dermatomyositisMedicineRheumatologyPrednisoneClinical trialInternal medicinePhysical therapyRheumatismRandomized controlled trialRituximabDermatomyositis

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop response criteria for adult dermatomyositis (DM) and polymyositis (PM). METHODS: Expert surveys, logistic regression, and conjoint analysis were used to develop 287 definitions using core set measures. Myositis experts rated greater improvement among multiple pairwise scenarios in conjoint analysis surveys, where different levels of improvement in 2 core set measures were presented. The PAPRIKA (Potentially All Pairwise Rankings of All Possible Alternatives) method determined the relative weights of core set measures and conjoint analysis definitions. The performance characteristics of the definitions were evaluated on patient profiles using expert consensus (gold standard) and were validated using data from a clinical trial. The nominal group technique was used to reach consensus. RESULTS: Consensus was reached for a conjoint analysis-based continuous model using absolute percent change in core set measures (physician, patient, and extramuscular global activity, muscle strength, Health Assessment Questionnaire, and muscle enzyme levels). A total improvement score (range 0-100), determined by summing scores for each core set measure, was based on improvement in and relative weight of each core set measure. Thresholds for minimal, moderate, and major improvement were ≥20, ≥40, and ≥60 points in the total improvement score. The same criteria were chosen for juvenile DM, with different improvement thresholds. Sensitivity and specificity in DM/PM patient cohorts were 85% and 92%, 90% and 96%, and 92% and 98% for minimal, moderate, and major improvement, respectively. Definitions were validated in the clinical trial analysis for differentiating the physician rating of improvement (P < 0.001). CONCLUSION: The response criteria for adult DM/PM consisted of the conjoint analysis model based on absolute percent change in 6 core set measures, with thresholds for minimal, moderate, and major improvement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.028
metaresearch head score (Gemma)0.035
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 score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.007

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 head0.056
GPT teacher head0.415
Teacher spread0.359 · 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

Citations82
Published2017
Admission routes1
Has abstractyes

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