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Record W2946183643 · doi:10.1002/mus.26521

Investigation of the psychometric properties of the inclusion body myositis functional rating scale with rasch analysis

2019· article· en· W2946183643 on OpenAlexfundno aff
Gita Ramdharry, Jasper M. Morrow, Stacie Hudgens, Iwona Skorupinska, Kelly Gwathmey, Melissa Currence, Laura Herbelin, Omar Jawdat, Mamatha Pasnoor, April McVey, Richard J. Barohn, Ted M. Burns, Mazen M. Dimachkie, Anthony A. Amato, Michael G. Hanna, Pedro Machado

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsnot available
FundersMedical Research Council CanadaMedical Research CouncilNational Institute for Health and Care Research
KeywordsRasch modelRating scaleInclusion body myositisPsychometricsScale (ratio)Physical medicine and rehabilitationPsychologyPhysical therapyClinical psychologyMedicineMyositisDevelopmental psychologyInternal medicineCartography

Abstract

fetched live from OpenAlex

INTRODUCTION: The Inclusion Body Myositis Functional Rating Scale (IBMRFS) is a 10-item clinician-rated ordinal scale developed for people with inclusion body myositis. METHODS: Single observations of the IBMFRS were collected from 132 patients. After Rasch analysis, modifications were made to the scale to optimize fit to the Rasch model while maintaining clinical validity and utility. RESULTS: The original IBMFRS did not fit the assumptions of the Rasch model because of multidimensionality of the scale. Items assessed local dependence, disordered step thresholds, and differential item functioning. Deconstructing the scale into upper limb (IBMFRS-UL) and lower limb (IBMFRS-LL) scales improved fit to the Rasch model. A 9-item scale with the swallowing item removed (IBMFRS-9) remained multidimensional but demonstrated the ability to discriminate patients along the severity continuum. IBMFRS-UL, IBMFRS-LL, and IBMFRS-9 scores were transformed to a 0-100 scale for comparability. DISCUSSION: This analysis has led to the development of 3 optimized versions of the IBMFRS. Muscle Nerve 60: 161-168, 2019.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.113
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.198
Teacher spread0.186 · 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 teacher head, 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

Citations15
Published2019
Admission routes1
Has abstractyes

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