MSmonitor-plus program and video calling care (MPVC) for multidisciplinary care and self-management in multiple sclerosis: study protocol of a single-center randomized, parallel-group, open label, non-inferiority trial
Bibliographic record
Abstract
BACKGROUND: We designed a new multi-modal version of the MSmonitor, called the MSmonitor-Plus and Video calling Care (MPVC), a self-management and education program with e-health interventions that combines frequent use of specific questionnaires with video calling in treating multiple sclerosis (MS) patients. OBJECTIVE: To assess the effectiveness, cost-effectiveness and feasibility of MPVC compared to care as usual (CAU), with the goal of achieving equal or better quality of life for MS patients and their partners/informal caregivers. Our hypothesis is that by using MPVC, monitoring will become more efficient, that patients' self-efficacy, quality of life, and adherence to treatment will improve, and that they will be able to live their lives more autonomously. METHODS: A randomized, parallel-group, open label, non-inferiority trial will be conducted to compare MPVC with CAU in MS patients and their partners/informal caregivers. A total of 208 patients will be included with follow-up measurements for 2 years (at baseline and every 3 months). One hundred four patients will be randomized to MPVC and 104 patients to CAU. Partners/informal caregivers of both groups will be asked to participate. The study will consist of three parts: 1) a clinical effectiveness study, 2) an economic evaluation, and 3) a process evaluation. The primary outcome relates to equal or improved disease-specific physical and mental quality of life of the MS patients. Secondary outcomes relate to self-efficacy, efficiency, cost-effectiveness, autonomy, satisfaction with the care provided, and quality of life of partners/informal caregivers. DISCUSSION: The idea behind using MPVC is that MS patients will gain more insight into the individual course of the disease and get a better grip on their symptoms. This knowledge should increase their autonomy, give patients more control of their condition and enable them to better and proactively interact with health care professionals. As the consulting process becomes more efficient with the use of MPVC, MS-related problems could be detected earlier, enabling earlier multidisciplinary care, treatment or modification of the treatment. This could have a positive effect on the quality of life for both the MS patient and his/her partner/informal caregiver, reducing health and social costs. TRIAL REGISTRATION: NCT05242731 Clinical Trials.gov. Date of registration: 16 February 2022 retrospectively registered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".