Association between exon-skipping therapy with eteplirsen and cardiac outcomes in Duchenne muscular dystrophy
Bibliographic record
Abstract
Background Duchenne muscular dystrophy (DMD) leads to dilated cardiomyopathy and heart failure during teenage years or young adulthood. Eteplirsen promotes dystrophin production through skipping of exon 51 of the DMD gene. Objective This analysis compared LVEF decline between eteplirsen-treated and control patients with exon 51 skip-amenable DMD. Methods Eteplirsen-treated patients from clinical trials were matched with control patients from natural history studies in a propensity score analysis. Risk of reaching LVEF thresholds (50%, 55%, and 60%) was evaluated using Cox proportional hazard models. Annual rate of LVEF decline was characterised using linear mixed effects models. Results Among 141 eteplirsen-treated and 103 control patients available for matching, the analysis included 122 eteplirsen-treated patients matched with 122 control patients (64 unique control patients). No eteplirsen-treated and 27 controls (22.1%) reached LVEF <50%; eteplirsen-treated patients had a lower risk of reaching <55% and <60% thresholds versus controls (hazard ratio = 0.22; 95% CI = 0.07–0.66; P < 0.01 and hazard ratio = 0.40; 95% CI = 0.22–0.76; P < 0.01, respectively). Annual rate of LVEF decline for eteplirsen-treated and controls was −0.66% (95% CI = –0.96 to −0.36, P < 0.01) and −1.38% (95% CI = –1.60 to −1.16; P < 0.01), respectively. Results were consistent with a sensitivity analysis matching each eteplirsen-treated patient once with a unique control patient and with several tests for potential bias. Conclusions In this retrospective study, eteplirsen-treated patients were observed to have a significantly lower risk of reaching LVEF thresholds indicative of cardiac function decline and attenuation of LVEF decline compared with matched controls.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".