Health-Related Quality of Life in Children with Duchenne Muscular Dystrophy: A Review
Bibliographic record
Abstract
In pediatric chronic illness, improving health-related quality of life (HRQOL) has become one of the most important goals of disease management. Duchenne muscular dystrophy (DMD) is a debilitating, progressive and chronic neuromuscular disorder affecting boys. The purpose of this review is to provide an overview of published research on HRQOL in the pediatric DMD population, describe the instruments used and summarize the study findings. The databases searched were Medline, Embase and PsycInfo. The literature search yielded 167 articles, of which 19 were included in this review. The studies were published between 2005 and 2013 across nine countries. Thirteen different generic and disease-specific measures were used, the most common being the Pediatric Quality of Life 4.0 Generic Core module.HRQOL in boys with DMD is worse than that of healthy peers and children with other chronic illnesses, especially in the physical domains. Boys who are at a more severe stage of the disease reported worse physical HRQOL but not necessarily psychosocial HRQOL than boys at a less severe stage. Traditional clinical outcome measures correlated well only with physical HRQOL. Parents' proxy-reports of their sons' HRQOL and the boys' self-reports had poor concordance. More research is needed to assess trends in HRQOL over time and to elucidate factors that affect 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".