Sleep disordered breathing: Assessment and therapy in the age of emerging neuromuscular therapies
Bibliographic record
Abstract
The term neuromuscular disease (NMD) encompasses a large variety of disorders that result in abnormal muscle function. Although it may be conventional to relate the use of this term to the most common muscular diseases (Duchenne muscular dystrophy [DMD], spinal muscular atrophy [SMA], and amyotrophic lateral sclerosis, etc), it is important to extend the term to pathologies manifested by severe neurologic (brain and spinal cord) malformations and injuries. In many of these scenarios, there are common mechanisms that contribute to sleep disordered breathing (SDB) and respiratory insufficiency although comorbidities may be somewhat different. Advances in the understanding of these diseases and their natural history, and increasing availability of mechanical ventilation to these patients have improved survival. The development of novel genetic and molecular therapies (as in the cases of DMD, SMA, and X-linked myotubular myopathy) provides an opportunity to use SDB as a reasonable outcome measure while also allowing the use of polysomnography as a validation tool in the assessments of effectiveness of therapies. We seek to provide an understanding of SDB in NMDs, and in the same light, would like to begin the conversation of thinking about weaning respiratory support when possible.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".