Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a devastating disease that results in life-limiting complications such as loss of skeletal muscle function as well as respiratory and cardiac complications. Advanced therapeutics in pulmonary care have significantly reduced respiratory complication-related mortality, making cardiomyopathy the main determinant factor of survival. While there are multiple therapies such as the use of anti-inflammatory drugs, physical therapy, and ventilatory assistance targeted toward delaying the disease progression in DMD, a cure remains elusive. In the last decade, several therapeutic approaches have been developed to improve patient survival. These include small molecule-based therapy, micro-dystrophin gene delivery, CRISPR-mediated gene editing, nonsense readthrough, exon skipping, and cardiosphere-derived cell therapy. Associated with the specific benefits of each of these approaches are their individual risks and limitations. The variability in the genetic aberrations leading to DMD also limits the widespread use of these therapies. While numerous approaches have been explored to treat DMD pathophysiology, only a handful have successfully advanced through the preclinical stages. In this review, we summarize the currently approved as well as the most promising therapeutics undergoing clinical trials aimed toward treating DMD with a focus on its cardiac manifestations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".