Response heterogeneity in a randomized controlled trial of telerehabilitation interventions among adults with multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: Telerehabilitation may be effective on average but is not equally effective among all people with multiple sclerosis (MS). Thus, the purpose of this secondary analysis of a randomized controlled trial was to explore whether baseline characteristics of participants with MS influence fatigue and physical activity outcomes of three telerehabilitation interventions. METHODS: Participants were randomized to contact-control intervention (CC), physical activity-only intervention (PA-only), and physical activity plus fatigue self-management intervention (FM+). The 12-week interventions were delivered over the phone. Sociodemographic (age and income), clinical (comorbidities, mental function and physical function), psychosocial (self-efficacy, outcome expectations and goal-setting), and behavioural baseline characteristics (step count and fatigue self-management behaviors) were used in a moderated regression analysis and a responder analysis to examine their influence on the Fatigue Impact Scale (FIS) and Godin Leisure-Time Exercise Questionnaire (GLTEQ) at post-test (i.e. immediately post-interventions). RESULTS: < 0.05) between responders and non-responders. Specifically, non-responders on the FIS at post-test in the PA-only intervention had significantly lower baseline scores in goal setting for engaging in fatigue self-management behaviours. Also, non-responders on the GLTEQ at post-test in the FM+ intervention had significantly worse baseline scores in mental function. DISCUSSION: Further research is needed to understand the complex relationship among baseline characteristics, telerehabilitation and response heterogeneity. We discuss how research on examining response heterogeneity may be advanced by conducting mega-clinical trials, secondary analyses of big data, meta-analyses and employing non-traditional research designs. TRIAL REGISTRATION: Clinicaltrials.gov (NCT01572714).
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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 | 0.091 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".