ArticleBMC research notes2022
Systematic description of an interdisciplinary multimodal pain treatment programme for patients with chronic musculoskeletal pain, using the TIDieR checklist.
Article in BMC research notes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05661760 (Development of a Clinical Prediction Model to Facilitate Decision Making in Interdisciplinary Biopsychosocial Rehabilitation in Patients With Osteoarthritis), which is not on this map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development of a Clinical Prediction Model to Facilitate Decision Making in Interdisciplinary Biopsychosocial Rehabilitation in Patients With Osteoarthritis (OA)
Who cites it
7 citing papers in PubMed.
- Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation.JMIR rehabilitation and assistive technologies · 2025Article
- Article
- Clinical prediction model for interdisciplinary biopsychosocial rehabilitation in osteoarthritis patients.European journal of physical and rehabilitation medicine · 2024Article
- Article
- Use of Virtual Reality in Interdisciplinary Multimodal Pain Treatment With Insights From Health Care Professionals and Patients: Action Research Study.JMIR rehabilitation and assistive technologies · 2023Article
- Healthcare Professionals' Perceptions of and Attitudes towards a Standardized Content Description of Interdisciplinary Rehabilitation Programs for Patients with Chronic Pain-A Qualitative Study.International journal of environmental research and public health · 2023Article
- Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.Frontiers in pain research (Lausanne, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo provide a thorough and systematic description of an interdisciplinary multimodal pain treatment programme (IMPT) for patients with chronic musculoskeletal pain (CMP), using the TIDieR checklist as a guide.
resultsThe main goal of the 'Centre for Integral Rehabilitation (CIR) Excellent' IMPT is to improve daily functioning, participation and quality of life of patients with CMP by helping them to adapt their behaviour so as to better manage their symptoms. A combination of physical and psychosocial treatment methods is employed, including Emotional Awareness and Expression Therapy (EAET), Pain Neuroscience Education (PNE), Acceptance and Commitment Therapy (ACT), graded activity, exposure in vivo, and experiential learning through physical training. The interdisciplinary treatment team comprises physiotherapists, psychologists and a physiatrist. The programme lasts 10 weeks (61 h in total) and consists of three phases: a start (Week 1), education (Weeks 2-3), and skills learning phase (Weeks 4-10). Patients come in twice a week and participate in 2-4 sessions (3-4 h) per treatment day. The programme consists of both individual (physical and mental coaching) and group sessions (education, movement and behaviour outdoors/indoors). Individualisation through personal goal-setting is an important characteristic of the treatment, as well as frequent interdisciplinary consultation between care providers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.