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Enregistrement W3212315446 · doi:10.1182/blood-2021-151532

Early Findings on the Use of Motion Capture during Simulated Sports Activities to Better Understand Hemophilic Arthropathy

2021· article· en· W3212315446 sur OpenAlexaboutno aff
Beth Boulden Warren, Joseph Mah, Niamh Mah, Hana Durkee, Sharon Funk, Marilyn J. Manco‐Johnson, James Carollo

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueHemophilia Treatment and Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePhysical therapyAnkleBarefootJumpingPhysical medicine and rehabilitationArthropathySquatting positionHaemophiliaRange of motionHemarthrosisOsteoarthritisSurgery

Résumé

récupéré en direct d'OpenAlex

Abstract Many persons with hemophilia (PwH) have joint bleeding despite prophylaxis. We hypothesize that movement biomechanics play a significant and largely unexplored role in joint bleeding, which could be exploited to develop personalized rehabilitation programs. We have designed a clinical study to test this hypothesis. Here we show data from the first 3 enrollees as case studies of what could potentially be learned. Data were collected from 3 participants with hemophilia A, ages 10-30 years, on prophylaxis with emicizumab, with at least 1 joint bleed in the lower extremity in the past year, using motion capture techniques with force plates and reflective markers collected through a Vicon system. Motion lab activities were designed to simulate sports activities and included walking (barefoot and shod), squatting (double and single leg), hopping on one foot, and jumping from a 30-cm box. Two patients (B and C) routinely take additional factor VIII prophylaxis prior to physical activity and did so before coming to the motion lab. Hemophilia Joint Health Score (HJHS) was obtained by an experienced physical therapist prior to motion lab collection to gauge degree of hemophilic arthropathy by physical exam. A subset of participants and activities are shown graphically. Participant A (not shown) had a higher HJHS in the right knee than left (3 vs 0) and had HJHS of 4 in bilateral ankles, with most recent bleed in the right ankle 8 months prior; primary physical activity was walking. This participant showed subtle differences between the right and left in all activities, including less weight on the right leg during double leg squat, and right single leg hop lower than the left. Participant B had a higher HJHS in the right knee than the left (3 vs 0) and in the right ankle than the left (7 vs 6), and he had had multiple episodes of knee pain but no change in HJHS since 2 years prior. Knee MRIs performed outside of the study were consistent with tendinosis of bilateral quadriceps rather than bleeding. Primary sports were basketball, hiking, biking, and skiing. Participant B had subtle differences between the right and left side that seemed to protect the right, including mildly decreased right knee flexion with weight acceptance during walking (more pronounced when wearing shoes), lower power generation by the right ankle than the left in walking, and lower peak ground reaction force (GRF) on the right than left in forward hopping. Some motions seemed to protect the left side more, with less power generation and absorption by the ankle (double and single leg squats) and hip (single leg squat only), right single leg squat somewhat deeper than the left, and slower time to step onto the left foot than the right. Participant C had a higher HJHS in the right knee than left (2 vs 1) and in the right ankle than left (5 vs 3) but had had worsening HJHS in bilateral ankles compared to his previous scores, with bleeds in both knees in the past year. Primary physical activities were baseball (pitching), basketball, biking, skiing, and golf. He had recently been diagnosed with Osgood Schlatter (patellar tendon/tibial tuberosity inflammation) of the left knee, which had been causing pain for several months, but no bleeding in that knee. Movement analysis reflected left knee pain, including very little flexion with weight acceptance when walking; very little power absorption or generation from the knee in walking, hopping, and squatting; shallower squats and lower hops on the left; and lower ground reaction force on the left than the right with walking. Perhaps related to the left knee protection, both ankles were consistently more plantarflexed in walking, and there was increased power generation in the right ankle compared to the left. These findings suggest the presence of subtle asymmetry related to hemophilic arthropathy and previous bleeding, which were more pronounced in more strenuous activities than with walking. The results of participant C could suggest that pain, even if unrelated to hemophilia, could cause compensatory movement mechanisms that could lead to increased bleeding risk in other lower extremity joints. Ongoing analysis will include tracking of bleeding over 1 year following motion analysis, enrollment of additional participants, comparison with controls, and performing detailed statistical analysis to determine which movement parameters correlate best with HJHS and lower extremity bleeding risk. Figure 1 Figure 1. Disclosures Warren: Novo Nordisk: Consultancy; Hema Biologics: Consultancy; Bayer: Research Funding; CSL Behring: Research Funding; Genentech: Research Funding. Funk: Biomarin: Consultancy; Sanofi Genzyme: Speakers Bureau; Toronto Sick Kids Hospital: Patents & Royalties: Hemophilia Joint Health Score Royalties; Partners: Honoraria.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,043
Tête enseignante GPT0,262
Écart entre enseignants0,218 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2021
Routes d'admission1
Résumé présentoui

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