The relationship between clinical examination measures and ultrasound measures of fascia thickness surrounding trunk muscles or lumbar multifidus fatty infiltrations: An exploratory study
Notice bibliographique
Résumé
Abstract Patients with chronic low back pain (CLBP) exhibit remodelling of the lumbar soft tissues such as muscle fatty infiltrations (MFI) and fibrosis of the lumbar multifidus (LuM) muscles, thickness changes of the thoracolumbar fascia (TLF) and perimuscular connective tissues (PMCT) surrounding the abdominal lateral wall muscles. Rehabilitative ultrasound imaging (RUSI) parameters such as thickness and echogenicity are sensitive to this remodelling. This experimental laboratory study aimed to explore whether these RUSI parameters (LuM echogenicity and fascia thicknesses), hereafter called dependent variables (DV) were linked to independent variables (IV) such as (1) other RUSI parameters (trunk muscle thickness and activation) and (2) physical and psychological measures. RUSI measures, as well as a clinical examination comprising physical tests and psychological questionnaires, were collected from 70 participants with LBP. The following RUSI dependent variables (RUSI‐DV), measures of passive tissues were performed bilaterally: (1) LuM echogenicity (MFI/fibrosis) at three vertebral levels (L3/L4, L4/L5 and L5/S1); (2) TLF posterior layer thickness, and (3) PMCT thickness of the fasciae between subcutaneous tissue thickness (STT) and external oblique (PMCT STT/EO ), between external and internal oblique (PMCT EO/IO ), between IO and transversus abdominis (PMCT IO/TrA ) and between TrA and intra‐abdominal content (PMCT TrA/IA ). RUSI measures of trunk muscle's function (thickness and activation), also called measures of active muscle tissues, were considered as independent variables (RUSI‐IV), along with physical tests related to lumbar stability ( n = 6), motor control deficits ( n = 7), trunk muscle endurance ( n = 4), physical performance ( n = 4), lumbar posture ( n = 2), and range of motion (ROM) tests ( n = 6). Psychosocial measures included pain catastrophizing, fear‐avoidance beliefs, psychological distress, illness perceptions and concepts related to adherence to a home‐based exercise programme (physical activity level, self‐efficacy, social support, outcome expectations). Six multivariate regression models (forward stepwise selection) were generated, using RUSI‐DV measures as dependent variables and RUSI‐IV/physical/psychosocial measures as independent variables (predictors). The six multivariate models included three to five predictors, explaining 63% of total LuM echogenicity variance, between 41% and 46% of trunk superficial fasciae variance (TLF, PMCT STT/EO ) and between 28% and 37% of deeper abdominal wall fasciae variance (PMCT EO/IO , PMCT IO/TrA and PMCT TrA/IA ). These variables were from RUSI‐IV (LuM thickness at rest, activation of IO and TrA), body composition (percent fat) and clinical physical examination (lumbar and pelvis flexion ROM, aberrant movements, passive and active straight‐leg raise, loaded‐reach test) from the biological domain, as well as from the lifestyle (physical activity level during sports), psychological (psychological distress—cognitive subscale, fear‐avoidance beliefs during physical activities, self‐efficacy to exercise) and social (family support to exercise) domains. Biological, psychological, social and lifestyle factors each accounted for substantial variance in RUSI‐passive parameters. These findings are in keeping with a conceptual link between tissue remodelling and factors such as local and systemic inflammation. Possible explanations are discussed, in keeping with the hypothesis‐generating nature of this study (exploratory). However, to impact clinical practice, further research is needed to determine if the most plausible predictors of trunk fasciae thickness and LuM fatty infiltrations have an effect on these parameters.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».