Shoulder injuries in Facioscapulohumeral muscular dystrophy patients (P6-8.001)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».