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Record W2045303979 · doi:10.1002/jmri.22849

Quantitative assessment of skeletal muscle degeneration in patients with myotonic dystrophy type 1 using MRI

2011· article· en· W2045303979 on OpenAlexaff
Bassem Hiba, Nathalie Richard, Luc J. Hébert, Chantal Côté, Mimoun Nejjari, C. Vial, Françoise Bouhour, Jack Puymirat, M. Janier

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2011
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversité LavalCanadian Armed Forces
Fundersnot available
KeywordsMedicineMyotonic dystrophyAnkleMuscle atrophyAtrophyMagnetic resonance imagingMuscular dystrophyNuclear medicineAnatomyPathologyInternal medicineRadiology

Abstract

fetched live from OpenAlex

PURPOSE: To identify MRI biomarkers that could be used to follow disease progression and therapeutic efficacy in one individual muscle in patients with myotonic dystrophy type 1 (DM1). MATERIALS AND METHODS: Lower limb MRI and maximal ankle dorsiflexor strength assessment, using a hand-held dynamometer, were performed in 19 DM1 patients and 6 control subjects. The volume of residual muscle tissue of Tibialis Anterior (TA) muscle was chosen as an index for muscle atrophy, and the T2-relaxation-time of the residual muscle tissue was measured to evaluate edema-like lesions. The fat-to-water ratio was assessed using three-point Dixon images to quantify fat infiltration in the entire muscle. RESULTS: The intra-observer variability of MRI indices (∼5.2% for the residual muscle tissue volume and 2.5% for the fat-to-water ratio) was lower than that of the dorsiflexor torque measurement (∼11.5%). A high correlation (r = 0.91) was found between maximal ankle dorsiflexor strength and residual TA muscle tissue volume in DM1 patients. Increases in the fat-to-water ratio and T2-relaxation-time were associated with a decrease in maximal ankle dorsiflexor strength. CONCLUSION: MRI appears as a noninvasive method which can be used to follow disease progression and therapeutic efficacy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.525

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 head0.032
GPT teacher head0.288
Teacher spread0.255 · 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

Citations63
Published2011
Admission routes1
Has abstractyes

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