Modelling the epidemiology of Duchenne muscular dystrophy provides insights into the overall population and selected subpopulations in nine countries
Bibliographic record
Abstract
Abstract Purpose Duchenne muscular dystrophy (DMD) is a rare X-linked neuromuscular disease caused by mutations in the DMD gene, leading to progressive muscle weakness, diminished quality of life and premature death. We aimed to develop an epidemiology model for DMD, providing country-specific prevalence estimates of the total diagnosed population and selected subpopulations. Methods To estimate DMD prevalence, country-specific inputs were derived from population statistics and a literature review of epidemiological data. Diagnosed incidences, considering historic trends, were applied to the number of effective live male births each year from 1950 onwards. Newly diagnosed patients entered the model based on the distribution of the age of diagnosis and patients left the model based on birth cohort-dependent survival. The estimated total number of individuals with DMD was calculated for each calendar year as the sum of patients from previous years who had not left the model owing to death and newly diagnosed patients. To assess validity, estimated DMD prevalence was compared with published prevalence. Results For the year 2023, the estimated total number of individuals with DMD was 5249 in Brazil, 1003 in Canada, 27 931 in China, 2082 in France, 2071 in Germany, 1361 in Italy, 3110 in Japan, 1169 in Spain and 2055 in the UK. Age group distribution was comparable across countries, with an approximately equal representation of paediatric and adult populations. Projections of total number of patients with DMD for each country from the year 2023 to 2040 showed variable trends. In European countries, 65% of patients with DMD were estimated to be non-ambulatory. The model-estimated prevalence showed good alignment with published prevalence. Conclusion These findings provide robust prevalence estimates that increase our understanding of the epidemiology 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".