P.078 Health-related quality of life and fatigue in children with Duchenne muscular dystrophy: A three-year longitudinal study
Bibliographic record
Abstract
Background: Longitudinal data on health-related quality of life (HRQOL) and fatigue in paediatric Duchenne muscular dystrophy (DMD) are limited. Recently, fatigue was reported to be the greatest predictor of poor HRQOL in paediatric DMD. Understanding the trajectory of HRQOL and its relationship with fatigue may facilitate the development of improved therapeutic strategies. Our objective was to describe three-year changes in HRQOL and fatigue in children with DMD. Methods: Patients identified via the Canadian Neuromuscular Disease Registry received mailed questionnaires (2013–2016). HRQOL was assessed using the PedsQLTM GCS and NMM domains, and fatigue was assessed using the MFS domain (patient- and parent-report). Mean three-year change in scores were computed. Pearson correlations were computed between three-year change in HRQOL and fatigue. Results: Mean decline in MFS scores for patient- and parent-reports were 1.03 and 1.19, respectively. Mean decline in GCS scores for patient- and parent-report were 1.75 and 4.13, respectively. Mean change in NMM scores for patient- and parent-report were 0.72 and -8.36, respectively. Change in MFS score was associated with changes in GCS (r=0.72, p<0.001) and NMM scores (r=0.84, p<0.001) by patient-report. Conclusions: Children with DMD experience worse fatigue and HRQOL over time. Parents perceive a greater decline in HRQOL over time compared to patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".