Duchenne muscular dystrophy respiratory profiles from real world registry data
Bibliographic record
Abstract
INTRODUCTION: Understanding real-world profiles from neuromuscular databases is helpful for optimizing clinical care and planning research studies. The Canadian Neuromuscular Disease Registry (CNDR) has respiratory data from a population of boys with Duchenne Muscular Dystrophy (DMD). OBJECTIVES: To describe cross-sectional respiratory profiles from a national DMD real-world dataset. To explore the relationship between forced vital capacity percent predicted (FVC%) and disease severity parameters: scoliosis, ambulation and ventilation status. METHODS: Descriptive statistics summarized the respiratory profiles. The CNDR registry enrolls and collects DMD clinic data from 36 Canadian centers. RESULTS: There were 414 participants enrolled. The age ranged from 2 to 36 years old. Pulmonary function test data were available for 323 participants. The use of ventilatory support was seen in a significant proportion (19.5%) of subjects by age 14-16 years and was used by the majority (69.2%) by age 20-22 years. FVC% declined at a rate of 3.19% per year with every 1-year increase in age. FVC% declined annually by 2.47% in nonambulatory participants versus by 0.96% in ambulatory participants. FVC% did not significantly change over age with the presence of scoliosis or use of ventilatory technology. CONCLUSIONS: The data from this large cohort are valuable for understanding real-world patterns of clinical care and disease progression. There is a significant association between the loss of ambulation and the rate of FVC% decline. Further longitudinal studies are needed to better understand the impact of disease parameters on pulmonary function decline and the need for ventilatory support.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".