Moving neuromuscular disorders research forward: from novel models to clinical studies
Bibliographic record
Abstract
ABSTRACT Neuromuscular disorders (NMDs) encompass a diverse group of genetic diseases characterized by loss of muscle functionality. Despite extensive efforts to develop therapies, no curative treatment exists for any of the NMDs. For multiple disorders, however, therapeutic strategies are currently being tested in clinical settings, and the first successful treatments have now entered clinical practice (e.g. spinraza for spinal muscular atrophy). Successful clinical translation depends on the quality and translatability of preclinical findings and on the predictive value of the experimental models used in their initial development. This Special Issue of Disease Models & Mechanisms has a particular focus on translational research for NMDs. The collection includes original research focusing on advances in the development of novel in vitro and in vivo models, broader understanding of disease pathology and progression, and approaches to modify the disease course in these models. We also present a series of special articles and reviews that highlight our understanding of cellular mechanisms, biomarkers to tract disease pathology, the diversity of mouse models for NMDs, the importance of high-quality preclinical studies and data validation, and the pitfalls of successfully moving a potential therapeutic strategy to the clinic. In this Editorial, we summarize the highlights of these articles and place their findings in the broader context of the NMD research field.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".