Clinical, Psychosocial, and Central Correlates of Quality of Life in Myotonic Dystrophy Type 1 Patients
Bibliographic record
Abstract
AIMS: To identify sociodemographic, clinical, and central correlates of health-related quality of life (HRQoL) in DM1 patients. METHODS: 200 DM1 patients had assessments of muscular impairment, CTG repeats, and intelligence. Validated instruments were used to assess sociodemographic and clinical factors as well as social support, social participation, daytime sleepiness, fatigue, personality, mood, and quality of life. Regression analysis was used to identify correlates of SF-36 physical and mental component summary scores. RESULTS: Patients scored lower on all SF-36 physical health subscales compared with normative data but did not differ with respect to mental health function. Regression analysis revealed that psychological distress, fatigue, severe muscular impairment, emotional stability, not having worked within the last 12 months, and lower intellectual quotient were associated with lower scores in physical health function. Moreover, neuroticism, daytime sleepiness, dissatisfaction with social participation, and lower conscientiousness were associated with lower scores in mental health function. CONCLUSION: DM1 has an impact on SF-36 physical summary scores but not on mental summary scores. Factors such as fatigue, daytime sleepiness, psychological distress, unemployment, and social participation dissatisfaction that significantly affect HRQoL in DM1 are amenable to treatment and psychosocial interventions, namely by providing care that integrate health, social, and community services.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".