Trends and racial disparities in muscular dystrophy deaths in the United States, 1983–1998: An analysis of multiple cause mortality data
Bibliographic record
Abstract
To identify trends and patterns associated with muscular dystrophy (MD)-associated deaths, we analyzed population-based data from death certificates in the Multiple Cause Mortality Files compiled by the National Center for Health Statistics. From 1983 to 1998, 14,499 deaths in the United States were associated with ICD-9 codes for MD. The mortality rate for MD in the general U.S. population over this time period was 0.365 per 100,000 persons per year. Stratification by age at death revealed a trimodal distribution with peaks at 0, 17, and 62 years. The male-to-female ratio varied with age at death, a pattern consistent with a mixture of autosomal and X-linked MDs with different prognoses. Deaths related to MD appeared to be equally divided between presumed autosomal and X-linked MDs. The mortality rate was higher in Whites than in Blacks, for both autosomal and X-linked MDs. The median age at death was lower in Blacks than Whites for both males and females. Cardiac complications were more commonly noted among MD-associated deaths in Blacks (38.9%) than Whites (28.6%). Respiratory infections were noted in about 20% of MD-associated deaths and were more common in winter than summer months. Potential reasons for the racial differences include differences in prevalence rates, rates of diagnosis, and reporting on death certificates. Additional studies are needed to resolve these issues. Challenges in the interpretation of these data include the lack of ICD-9 codes specific for individual MDs and potential recording biases in underlying cause of death and contributing factors. We also present a method for estimating autosomal and X-linked contributions to the overall mortality rate of a genetically heterogeneous condition such as MD.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".