Personalized, Web-Based, Guided Self-Help for Patients With Medically Unexplained Symptoms in Primary Care: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Medically unexplained symptoms (MUS) constitute a major health problem because of their high prevalence, the suffering and disability they cause, and the associated medical costs. Web-based interventions may provide an accessible and convenient tool for managing MUS. We developed a personalized, Web-based, guided self-help intervention for MUS in primary care (Grip self-help) and would compare its effectiveness with that of usual care. OBJECTIVE: This paper aims to describe the rationale, objectives, and design of a pragmatic randomized controlled trial (RCT) assessing the effectiveness of Grip self-help. METHODS: For a pragmatic multicenter RCT, 165 adult patients with mild to moderate MUS will be recruited through general practices in the Netherlands. Randomization will be performed at general practice level. Over the course of several months, patients in the intervention group will receive a personalized set of Web-based self-help exercises, targeting the unhelpful cognitions, emotions, behaviors, and social factors that are relevant to them. The intervention is guided by a general practice mental health worker. The control group will receive care-as-usual. Primary outcome is physical health-related quality of life (RAND-36 or 36-item general health survey, physical component score). Secondary outcomes include severity of physical and psychological symptoms, mental health-related quality of life, cost-effectiveness, and acceptability. Assessments will take place at baseline, end of treatment, and at 16-, 26-, and 52-week follow-ups. RESULTS: Recruitment started in December 2018, and enrolment is ongoing. The first results are expected to be submitted for publication in December 2021. CONCLUSIONS: To our knowledge, this is the first study to combine the concepts of electronic health, self-help, and personalized medicine in the treatment of MUS. By improving the quality of life and reducing symptoms of patients with MUS, Grip self-help has the potential to reduce costs and conserve scarce health care resources. TRIAL REGISTRATION: Dutch Trial Register NTR7390; https://www.trialregister.nl/trial/7390. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13738.
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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 | 0.046 | 0.037 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.086 | 0.014 |
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".