A real-world longitudinal study to implement digital assessment and treatment for psychological distress in multiple sclerosis (MS): The COMPASS-MS study protocol (Preprint)
Bibliographic record
Abstract
Background: Comorbid anxiety and depression in patients with multiple sclerosis (MS) are common, conferring a greater risk of poorer outcomes and increased health care costs. Few MS services include scalable treatment pathways for psychological distress. Objective: This study aims to conduct a real-world longitudinal study evaluating the implementation and potential effectiveness of an integrated pathway involving digital screening for psychological distress and COMPASS-MS, a therapist-guided digital cognitive behavioral therapy tailored to the challenges of living with MS. Methods: This is a mixed methods, observational, real-world, longitudinal study being conducted in the United Kingdom. Routine mental health screening in the MS clinic will identify patients experiencing distress (using predefined clinical cutoffs), who will be assessed for eligibility for psychological treatment, including the COMPASS-MS program. Participants will receive COMPASS-MS online over approximately 12 weeks (including up to six 30-min therapist sessions). The implementation, reach, and adoption of the treatment pathway within a specialist MS service will be assessed using aggregate data on the uptake of mental health screening, eligibility, and consent rates for COMPASS-MS, as well as the number of COMPASS-MS sessions completed. Interviews with patients and health care professionals will primarily assess the scalability and potential or actual barriers and facilitators of the new pathway. Potential effectiveness will be assessed using participant questionnaires at preintervention and 12 weeks postintervention. The primary effectiveness outcome will be pre-post changes in distress (Patient Health Questionnaire Anxiety and Depression Scale scores). Quantitative data will be summarized using descriptive statistics and mixed effects models. Qualitative data will be analyzed using reflexive thematic and framework analysis. Results: Recruitment of both patients and health care professionals began in June 2024. For patients, recruitment was completed as of September 2025, including 82 participants. For health care professionals and stakeholders, as of September 2025, 7 had consented and completed interviews, and 13 more were expected to be recruited by August 2026. Data analysis has not yet started; however, quantitative results are expected by September 2026. Conclusions: The study findings will inform treatment pathways that can be incorporated into MS clinics and highlight adaptations or implementation protocols required to increase future scalability and effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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 source (direct Gemma or distilled Codex), 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".