Muscle MRI Pattern in Dysferlinopathy and its Correlation with Dysferlin Gait
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Magnetic resonance imaging (MRI) in dysferlinopathy has consistently demonstrated a particular pattern of affliction. We aimed to study muscle MRI characteristics of lower limbs in limb girdle muscular dystrophy (LGMD)-R2 phenotypes and correlate them with the gait pattern. METHODS: Forty genetically and/or biopsy-proven cases of dysferlinopathy underwent muscle MRI of the lower limbs. The pattern and extent of fatty infiltration and edema were recorded. Spearman's correlation analysis was used to find the correlation between muscle involvement and demographics, duration of illness, Muscular Dystrophy Functional Rating Scale (MDFRS), genotype, and gait pattern. RESULTS: Mean age at onset and duration of illness at evaluation were 21.5 ± 6.3 years and 7.15 ± 4.95 years, respectively. Male: Female of patients was 2:1. Long head of biceps femoris was most severely involved with relative sparing of short head. Specific MRI pattern was noted based on phenotype, though no genotypic correlation was observed. Adductor magnus and semimembranosus were more severely involved in LGMD and proximodistal (PD) forms compared to Miyoshi muscular dystrophy type 1 phenotype. In addition, tibialis posterior and extensor hallucis longus were more severely involved in PD compared to MM and LGMD phenotypes. MDFRS mobility domain and duration of illness correlated with MRI findings. Gait pattern analysis revealed more severe involvement of flexor hallucis longus compared to extensor hallucis longus. CONCLUSIONS: Muscle involvement differed based on the phenotype. Characteristic great toe extension in PD phenotype showed an imaging correlation with more severe involvement of flexor hallucis longus compared to extensor hallucis longus. Thus, imaging can be a potential biomarker to study the evolution and severity of disease in dysferlinopathy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".