Shoulder injuries in Facioscapulohumeral muscular dystrophy patients (P6-8.001)
Bibliographic record
Abstract
Objective: To describe the types, prevalence, and history of shoulder injuries in patients with facioscapulohumeral muscular dystrophy (FSHD). Background: FSHD is a dominantly inherited, slowly progressive muscular dystrophy with variable clinical manifestations. It usually initially presents as weakness in the face, shoulder, and upper extremities, eventually extending to the lower body. MRI studies have shown that wasting in the shoulder tends to follow a muscle-specific pattern, but to date there are no studies investigating shoulder injuries as an outcome, nor their impact on patients. Design/Methods: We distributed a cross-sectional anonymous web-administered survey of adult FSHD patients identified through the US National FSHD Registry and the FSHD Society registry. The survey collected FSHD history, functional status, severity scores, and detailed history of shoulder injury (defined as “any shoulder condition that caused pain, stiffness, or dysfunction in addition to that from FSHD”). Results: We identified 613 FSHD patients ranging from 18 to 91 years old (M=56.4, SD=14.7). Average age of symptom onset and diagnosis were 24 and 35, respectively, with an average Brooke Upper Extremity Score of 2.7 (SD=1.03). 89% of patients reported current shoulder weakness, while 34% reported a history of shoulder injury. The most-reported injuries were rotator cuff tendinitis/tendinopathy and rotator cuff tear, both with a prevalence of 9.8% – followed by subacromial bursitis (7.1%), shoulder impingement (6.0%), and dislocated shoulder (5.0%). Of the patients who reported a history of at least one shoulder injury: 42% believed that FSHD was entirely to blame, and 49% rated the impact of their injury on their life – at its worst – at 8/10 or higher. Conclusions: By exploring the previously undescribed burden of shoulder injuries in the FSHD patient population, this study may improve patients’ understanding of their condition and inform clinicians’ approach to monitoring the disorder. Disclosure: Mr. Liu has nothing to disclose. Dr. Tawil has received personal compensation in the range of $500-$4,999 for serving as a Consultant for DYNE Therapeutics. Dr. Tawil has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Arrowhead Pharma. Dr. Tawil has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Fulcrum Therapeutics. Dr. Tawil has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Acceleron Pharma. Dr. Tawil has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for MT Pharma. Dr. Tawil has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Roche Pharma. Dr. Tawil has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for miRecule. The institution of Dr. Tawil has received research support from Friends of FSH Research. The institution of Dr. Tawil has received research support from FSH Society. The institution of Dr. Tawil has received research support from NIH. The institution of Dr. Tawil has received research support from Fulcrum. Dr. Tawil has received intellectual property interests from a discovery or technology relating to health care. Dr. Tawil has received publishing royalties from a publication relating to health care. Dr. Statland has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Arrowhead. Dr. Statland has received personal compensation in the range of $500-$4,999 for serving as a Consultant for ML Bio. Dr. Statland has received personal compensation in the range of $500-$4,999 for serving as a Consultant for MT Pharma. Dr. Statland has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Epic Bio. Dr. Statland has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Armatus . Dr. Statland has received personal compensation in the range of $10,000-$49,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Dyne Therapeutics. Dr. Statland has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Avidity . Dr. Statland has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Fulcrum Therapeutics. Dr. Statland has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Roche. The institution of Dr. Statland has received research support from NIH. The institution of Dr. Statland has received research support from FSHD Society. The institution of Dr. Statland has received research support from Friends of FSH Research. The institution of Dr. Statland has received research support from FSHD Canada. The institution of Dr. Statland has received research support from MDA.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".