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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".