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Record W3100329125 · doi:10.1093/braincomms/fcaa191

Diagnostic modelling and therapeutic monitoring of immune-mediated necrotizing myopathy: role of electrical myotonia

2020· article· en· W3100329125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBrain Communications · 2020
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMyopathyMedicineDermatomyositisMyotoniaPolymyositisInternal medicineMyotonia congenitaMitochondrial myopathyCreatine kinasePathologyGastroenterologyImmunologyMyotonic dystrophyBiology

Abstract

fetched live from OpenAlex

Abstract Delayed diagnosis of immune-mediated necrotizing myopathy leads to increased morbidity. Patients with the chronic course without 3-hydroxy-3-methylglutaryl-coenzyme-A reductase-IgG or signal recognition particle-IgG are often challenging to diagnose. Immunotherapy response can also be difficult to assess. We created a statistical model to assist immune-mediated necrotizing myopathy diagnosis. Electrical myotonia versus fibrillations were reviewed as biomarkers for immunotherapy treatment response. Identified were 119 immune-mediated necrotizing myopathy cases and 938 other myopathy patients. Inclusion criteria included all having electrophysiological evaluations, muscle biopsies showing inflammatory/necrotizing myopathies, comprehensively recorded neurological examinations, and creatine kinase values. Electrical myotonia was recorded in 56% (67/119) of retrospective and 67% (20/30) of our validation immune-mediated necrotizing myopathy cohorts, and significantly (P < 0.001) favoured immune-mediated necrotizing myopathy over other myopathies: sporadic inclusion body myositis (odds ratio = 4.78); dermatomyositis (odds ratio = 10.61); non-specific inflammatory myopathies (odds ratio = 8.46); limb-girdle muscular dystrophies (odds ratio = 5.34) or mitochondrial myopathies (odds ratio = 14.17). Electrical myotonia occurred in immune-mediated necrotizing myopathy seropositive (3-hydroxy-3-methylglutaryl-coenzyme-A reductase-IgG 70%, 37/53; signal recognition particle-IgG 29%, 5/17) and seronegative (51%, 25/49). Multivariate regression analysis of 20 variables identified 8 (including electrical myotonia) in combination accurately predicted immune-mediated necrotizing myopathy (97.1% area-under-curve). The model was validated in a separate cohort of 30 immune-mediated necrotizing myopathy cases. Delayed diagnosis of cases with electrical myotonia occurred in 24% (16/67, mean 8 months; range 0–194). Half (8/19) had a chronic course and were seronegative, with high model prediction (>86%) at the first visit. Inherited myopathies were commonly first suspected in them. Follow-up evaluation in patients with electrical myotonia on immunotherapy was available in 19 (median 21 months, range 2–124) which reduced from 36% (58/162) of muscles to 7% (8/121; P < 0.001). Reduced myotonia correlated with immunotherapy response in 64% (9/14) as well as with median creatine kinase reduction of 1779 U/l (range 401–9238, P < 0.001). Modelling clinical features with electrical myotonia is especially helpful in immune-mediated necrotizing myopathy diagnostic suspicion among chronic indolent and seronegative cases. Electrical myotonia favours immune-mediated necrotizing myopathy diagnosis and can serve as an adjuvant immunotherapy biomarker.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.451

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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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