Effectiveness and Cost-Effectiveness of a Stepped Model of Care for Musculoskeletal Disorders: Protocol for a Multiarm Randomized Controlled Trial (Edu-First Trial)
Notice bibliographique
Résumé
BACKGROUND: Musculoskeletal disorders (MSKDs) are a leading cause of pain and disability, placing a substantial burden on health care systems. Optimizing resource use through innovative interventions is essential. Evidence from randomized controlled trials suggests that not all individuals with MSKDs require ongoing follow-up with a health care provider; for many, education alone is sufficient for symptom resolution. A stepped care model, which prioritizes patient education as a first-line intervention and reserves usual care for those with persistent symptoms, may enhance health care efficiency and reduce costs. OBJECTIVE: The primary objective of this randomized controlled trial is to evaluate the effectiveness of a stepped care model compared to the 2 most common approaches for managing MSKDs: usual medical care and usual rehabilitation care. A secondary objective is to assess cost-effectiveness. METHODS: This pragmatic, noninferiority, multiarm, parallel-group randomized controlled trial will enroll 369 adults with MSKDs, randomly assigned to one of three 12-week intervention groups: stepped care, usual medical care (physician-led), or usual rehabilitation care (physiotherapist-led). Participants in the stepped care group will first complete a 6-week education program. Those with persistent symptoms after 6 weeks will receive rehabilitation interventions, while participants whose symptoms have resolved will receive no further intervention. The primary outcome is functional limitations at 24 weeks. Secondary outcomes include pain severity, health-related quality of life, pain-related fear, and pain self-efficacy, assessed at baseline and at 6, 12, and 24 weeks. Linear mixed models will be used for group comparisons, and incremental cost-effectiveness analyses will evaluate cost-effectiveness. The ethics committee of the CIUSSS-CN approved the project (#2024-2982). Findings will be shared through clinical and community platforms, peer-reviewed publications, and conference presentations. RESULTS: The Edu-First trial is funded by a project grant from the Canadian Institutes of Health Research (grant #495615). Recruitment began on January 31, 2025. As of September 2025, a total of 65 participants have been enrolled. Recruitment is expected to continue for up to 3 years, targeting approximately 10 new participants per month, and is anticipated to be completed by Winter 2028. CONCLUSIONS: We anticipate that the stepped care model will be noninferior to usual medical care and usual rehabilitation care in terms of treatment effectiveness. Furthermore, it is expected to be cost-effective by reducing reliance on expensive resources, such as provider consultations and medical investigations. By emphasizing education and self-management as the initial approach, the stepped care model may enhance access to care without compromising quality, while empowering patients to actively manage their condition. Findings from this study could inform systemic changes in MSKD care delivery, improving treatment accessibility and reducing the average cost per care episode. TRIAL REGISTRATION: ClinicalTrial.gov NCT06832852; https://clinicaltrials.gov/ct2/show/NCT06832852. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77574.
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 | 0,040 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,009 | 0,003 |
| Méta-épidémiologie (sens large) | 0,013 | 0,011 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,008 |
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 ».