Evidence Regarding Duchenne Muscular Dystrophy Newborn Screening
Bibliographic record
Abstract
Variants in the DMD gene, located on the X chromosome, cause Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD). DMD reportedly affects about 2 per 10,000 newborn males, leading to progressive weakness and premature death, typically from respiratory or cardiac complications. The average age of diagnosis in the United States (US) over the past four decades has been 4.5 to 5 years. The availability of targeted therapies and the long diagnostic odyssey have led to advocacy for newborn screening (NBS). Studies of caregivers of children with DMD describe support for NBS. Meeting abstracts, which may have bias, suggest earlier identification in a child following DMD diagnosis in an older brother improves outcomes. Ohio and Minnesota include DMD NBS, and other states are planning implementation. DMD NBS is based on measuring the muscle isoform of creatine kinase (CK-MM), which is elevated due to muscle damage. Infants with borderline CK-MM levels can be retested after at least one week to determine if elevations are birth related. Molecular analysis in infants with significantly elevated CK-MM can identify DMD variants associated with DMD or BMD. Screening accuracy depends on the testing algorithm. Although treatment with glucocorticoids or related medications can improve outcomes for DMD despite side effects, the optimal age of initiation is unclear. Efficacy of the Food and Drug Administration-approved gene therapy has not been established, and it has a rare risk of hepatotoxicity. Genotype-specific exon-skipping medications, indicated for 27% of cases, may improve outcomes, but clinical benefit is not definitively established.
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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 | 0.029 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".