A.01 The relationship between fatigue and health-related quality of life in a clinical trial population of Duchenne muscular dystrophy patients
Bibliographic record
Abstract
Background: Fatigue was recently reported to be the largest contributor to poor health-related quality of life (HRQOL) in paediatric Duchenne muscular dystrophy (DMD). Additional studies are necessary to confirm the generalizability of this finding. Our objective was to explore the longitudinal relationship between fatigue and HRQOL in an additional cohort of DMD patients. Methods: We performed a secondary analysis of data from a clinical trial (NCT00592553), which enrolled patients with nonsense mutation DMD, aged 5–20 years, from 37 sites in 11 countries (N=174). Fatigue and HRQOL were assessed using the PedsQLTM Multidimensional Fatigue Scale and Generic Core Scales, respectively, by patient- and parent-report at baseline and over 48 weeks. Results: Patients reported greater fatigue than healthy controls from published data. There was no significant difference between patient- and parent-reported fatigue. Fatigue was significantly correlated with worse HRQOL at baseline, by patient-report (r=0.70, P<0.001) and parent-report (r=0.70, P<0.001); and at 48 weeks, by patient-report (r=0.79, P<0.001) and parent-report (r=0.74, P<0.001). Change in fatigue was significantly correlated with change in HRQOL over 48 weeks, by patient-report (r=0.64, P<0.001) and parent-report (r=0.67, P<0.001). Conclusions: Fatigue is a major contributor to HRQOL in DMD. The strong association between fatigue and HRQOL corroborates previous studies, and suggests that reducing fatigue may improve HRQOL.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".