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Record W3009574399 · doi:10.1002/mus.26859

Long‐term strength and functional status in inclusion body myositis and identification of trajectory subgroups

2020· article· en· W3009574399 on OpenAlexfundno aff
Alexander Oldroyd, James B Lilleker, J. Williams, Hector Chinoy, James Miller

Bibliographic record

VenueMuscle & Nerve · 2020
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsnot available
FundersManchester Biomedical Research CentreVersus ArthritisMedical Research Council CanadaMedical Research CouncilNational Institute for Health and Care Research
KeywordsInclusion body myositisTerm (time)Physical medicine and rehabilitationIdentification (biology)TrajectoryMedicineInclusion (mineral)MyositisPhysical therapyPsychologyInternal medicinePhysicsBiologySocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Objective information on longitudinal disease progression in inclusion body myositis (IBM) is lacking. METHODS: Longitudinal dynamometry and functional status data were collated from a cohort of IBM patients. Annual change was calculated by means of linear modeling. Trajectories of change in grip, knee extension, IBM Functional Rating Scale (IBM-FRS) and Neuromuscular Symptom Score (NSS) were identified by means of latent growth mixture modeling. RESULTS: Data were collated from 75 IBM patients (348 person-years follow-up). Annual strength loss was greatest for pinch (-10%) and knee extension (-4%). Functional deterioration was greatest for males. Three distinct trajectory groups were identified. Rapid deterioration trajectory for grip strength was associated with younger diagnosis age. Rapid deterioration for knee extension strength was associated with older age of diagnosis. DISCUSSION: This study has quantified strength change in IBM and identified distinct trajectory groups, which will aid prognostication and stratification for inclusion into future clinical trials.

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 score0.354
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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

Citations39
Published2020
Admission routes1
Has abstractyes

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