Longitudinal Outcomes Among Patients With Duchenne Muscular Dystrophy: A Canadian Retrospective Population‐Based Study
Bibliographic record
Abstract
AIMS: There are few long-term studies evaluating clinical outcomes and mortality among individuals with Duchenne muscular dystrophy (DMD); particularly using longitudinal health administrative claims data, reflecting populations managed in typical clinical practice. This study aimed to characterize DMD outcomes via a population-based database. METHODS: Patients with DMD, diagnosed between 01/1979 and 03/2020 at ≤ 10 years of age, were identified using the Manitoba Population Research Data Repository housed at the Manitoba Centre for Health Policy. De-identified longitudinal administrative data from 1998 to 2020 were used to retrospectively assess frequencies and age at first observation of key DMD outcomes including scoliosis, cardiovascular-related complications, severe respiratory-related morbidities, and mortality. Survival analyses using Kaplan-Meier curves were used to describe attrition and estimate probability of patients remaining observation-free by age. RESULTS: This study included 198 patients with median (IQR) follow-up of 9.6 (6.6-15.5) years. Corticosteroid use was observed in 26%, with a mean (SD) percentage of days covered of 31% (39%) from initiation to end of follow-up. Scoliosis observations were captured in 18% (median[IQR] age 12 [11-15] years at first observation), severe respiratory-related morbidities in 20% (14[6.5-18] years), and cardiovascular-related complications in 32% of the cohort (12.5[2-20.5] years). Mortality was observed in 14% of the cohort. Kaplan-Meier curves estimated 15% mortality by age 20 years and 20% by 25 years. DISCUSSION: In a population-based data set with decades of follow-up, these data provide longitudinal observations of the substantial burden of DMD, and insight into contemporary estimates of mortality and treatment patterns in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".