Observing the Clinical Course of Duchenne Muscular Dystrophy in Medicaid Real-World Healthcare Data
Bibliographic record
Abstract
INTRODUCTION: Duchenne muscular dystrophy (DMD) is a rare, severe progressive neuromuscular disease. Health insurance claims allow characterization of population-level real-world outcomes, based on observed healthcare resource use. An analysis of data specific to those with Medicaid insurance is presently unavailable. The objective was to describe the real-world clinical course of DMD based on claims data from Medicaid-insured individuals in the USA. METHODS: Individuals with DMD were identified from the MarketScan Multi-State Medicaid datasets (2013-2018). Diagnosis and procedure codes from healthcare claims were used to characterize the occurrence of DMD-relevant clinical observations; categories were scoliosis, cardiovascular-related, respiratory and severe respiratory-related, and neurologic/neuropsychiatric. Age-restricted analyses were conducted to focus on the ages at which DMD-relevant clinical observations were more likely to be captured, and to better understand the impact of both age and follow-up time. RESULTS: Of 2007 patients with DMD identified, median (interquartile range) age at index was 14 (9-20) years, and median follow-up was 3.1 (1.6-4.7) years. Neurologic and neuropsychiatric observations were most frequently identified, among 49.3% of the cohort; followed by cardiovascular (48.5%), respiratory (38.1%), scoliosis (36.3%), and severe respiratory (25.0%). Prevalence estimates for each category were higher when analyzed within age-restricted subgroups; and increased as follow-up time increased. CONCLUSIONS: This study is the first to use diagnosis and procedure codes from real-world Medicaid claims to document the clinical course in DMD. Findings were consistent with previously published estimates from commercially insured populations and clinical registries; and contribute to the expanding body of real-world evidence around clinical progression of patients with 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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".