P.007 Onset of facial weakness correlated with muscle strength in infantile facioscapulohumeral dystrophy (FSHD)
Bibliographic record
Abstract
Background: We investigated motor function associations with age, gender, and D4Z4 fragment size among participants with infantile FSHD. Methods: We collected standardized motor assessments including goniometry, manual muscle testing (MMT), quantitative muscle testing (QMT), and FSHD clinical severity scores (CSS) at 12 CINRG sites. To measure associations, we used linear regression models adjusted for age at enrollment, onset of weakness, gender, and D4Z4 repeats. Results: 53 participants (59% female, mean age 23.1±14.6 years) were enrolled. Weakness was most pronounced at the shoulder girdle and rectus abdominis (median MMT 30-38% of normal). Older enrollment age was associated with greater CSS (p=0.005) and reduced range of motion in shoulder abduction, shoulder flexion, elbow flexion, and ankle dorsiflexion (all p<0.01). Females and participants with larger D4Z4 repeats had milder shoulder/arm weakness and lesser disease severity (all p<0.05). Increased age at onset of facial weakness was significantly associated with greater total muscle strength, as measured by QMT and MMT (both p=0.002). Conclusions: We confirm the descending pattern of muscle involvement and milder disease severity in females or those with larger D4Z4 repeats. Furthermore, earlier age at onset of facial weakness was associated with greater muscle weakness. Future longitudinal assessments will describe rates of disease progression in this population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 0.001 |
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".