Clinical Outcome Evaluations and CBT Response Prediction in Myotonic Dystrophy
Bibliographic record
Abstract
BACKGROUND: The European OPTIMISTIC clinical trial has demonstrated a significant, yet heterogenous effect of Cognitive Behavioural Therapy (CBT) for Myotonic Dystrophy type 1 (DM1) patients. One of its remaining aims was the assessment of efficacy and adequacy of clinical outcome measures, including the relatively novel primary trial outcome, the DM1-Activ-c questionnaire. OBJECTIVES: Assessment of the relationship between the Rasch-built DM1-Activ-c questionnaire and 26 commonly used clinical outcome measurements. Identification of variables associated with CBT response in DM1 patients. METHODS: Retrospective analysis of the to date largest clinical trial in DM1 (OPTIMISTIC), comprising of 255 genetically confirmed DM1 patients randomized to either standard care or CBT with optionally graded exercise therapy. Correlations of 27 different outcome measures were calculated at baseline (cross-sectional) and of their respective intervention induced changes (longitudinal). Bootstrap enhanced Elastic-Net (BeEN) regression was validated and implemented to select variables associated with CBT response. RESULTS: In cross-sectional data, DM1-Activ-c correlated significantly with the majority of other outcome measures, including Six Minute Walk Test and Myotonic Dystrophy Health Index. Fewer and weaker significant longitudinal correlations were observed. Nine variables potentially associated with CBT response were identified, including measures of disease severity, executive cognitive functioning and perceived social support. CONCLUSIONS: The DM1-Activ-c questionnaire appears to be a well suited cross-sectional instrument to assess a variety of clinically relevant dimensions in DM1. Yet, apathy and experienced social support measures were less well captured. CBT response was heterogenous, requiring careful selection of outcome measures for different disease aspects.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".