D.09 Pharmacological therapy for the prevention and management of cardiomyopathy in Duchenne muscular dystrophy: a systematic review
Bibliographic record
Abstract
Background: Improved respiratory care of Duchenne muscular dystrophy (DMD) patients has unmasked cardiomyopathy as a major source of morbidity and mortality. There is currently no consensus regarding the management of DMD-associated cardiomyopathy (DMD-CM). The objective of this systematic review was to evaluate the efficacy of pharmacological therapies for prevention and management of DMD-CM, and determine the optimal timing to commence these interventions. Methods: A systematic search was conducted in October 2015 and updated in January 2016 using MEDLINE, EMBASE and CINAHL databases and 9 grey literature sources for studies evaluating the use of angiotensin-converting enzyme inhibitors (ACEi), angiotensin receptor blockers (ARB), beta-blockers (BB) or aldosterone antagonists (AA) in DMD patients. References of retrieved records were searched. Quality assessment was conducted using the Downs and Black Quality Assessment Checklist. PRISMA reporting guidelines were used. Results: The 11 included studies were of low methodological quality. However, the use of an ACEi, ARB, BB and AA tended to improve or preserve left ventricular systolic function and delay the progression of cardiomyopathy. Conclusions: While there is evidence supporting the use of heart failure medication in patients with DMD-CM, data regarding these interventions for delaying the onset of DMD-CM and when to initiate therapy is lacking.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".