P.113 A Population-based Study of the Epidemiology, Healthcare Resource Utilization and Costs of Duchenne Muscular Dystrophy in Alberta, Canada
Bibliographic record
Abstract
Background: Duchenne muscular dystrophy (DMD) is a severe progressive neuromuscular disease. This study aimed to estimate the prevalence, healthcare resource utilization (HRU), and medical costs of DMD in Alberta. Methods: This retrospective study linked provincial healthcare administrative data to identify patients with DMD utilizing a modified diagnostic code algorithm, including males <30 years of age. Five-year (April 2012 to March 2017) prevalence estimates were calculated and all-cause direct HRU and costs were examined in the first-year post-diagnosis. Results: Overall, 111 patients (median age: 12.0 years (IQR 4.7-18.3)) with DMD were identified. The estimated five-year period prevalence was 35.72 (95% CI 31.88-39.91) per 100,000 persons. All-cause HRU in the first-year post-diagnosis included a mean (SD) of 0.48 (1.19) hospitalizations (length of stay: 9.37 days (36.47)), 3.96 (6.16) general practitioner visits, 28.52 (62.98) specialist visits, and 20.14 (16.49) ambulatory care visits. Mean (SD) all-cause direct costs were $18,868 ($29,206) CAD in the first-year post-diagnosis. Conclusions: Patients with DMD had multiple interactions with the healthcare system in the year following diagnosis, resulting in substantial direct medical costs. More effective treatment strategies are needed to improve health outcomes and reduce the burden of DMD.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".