Health-related quality of life, pain, and fatigue in myotonic dystrophy type 2: a 13-year follow-up study
Bibliographic record
Abstract
PURPOSE: To examine the long-term progression of health-related quality of life (HRQoL), pain, and fatigue in myotonic dystrophy type 2 (DM2) compared to adult-onset myotonic dystrophy type 1 (DM1). MATERIALS AND METHODS: Data on HRQoL (Short Form 36 Health Survey (SF-36)), pain (McGill Pain Questionnaire), and fatigue (Checklist Individual Strength) were assessed in DM2 patients and age- and sex-matched DM1 patients, at baseline and after 13 years. RESULTS: Twenty-nine DM2 and 29 DM1 patients participated at baseline. Data of 18 DM2 and 16 DM1 patients were recollected at follow-up. Ten DM2 and 13 DM1 patients had passed away, and one DM2 patient did not consent participation. In DM2, mental health on SF-36 subscales (social functioning, mental health) decreased during follow-up without significant increase in pain or fatigue. In contrast, in DM1, both physical and mental health decreased, and pain and fatigue increased. CONCLUSIONS: HRQoL decreased over 13 years in DM2. Pain was present early in the DM2 disease course and did not increase significantly. Healthcare professionals should be aware of the long-term impact of myotonic dystrophy (DM) on pain, fatigue, physical and mental functioning, including social functioning. Symptomatic treatment of these aspects may decrease disease impact, because DM cannot be cured.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".