Within‐Breath Control of Genioglossal Muscle Activation in Humans: Effect of Sleep‐Wake State
Bibliographic record
Abstract
Pharyngeal dilator muscles are clearly important in the pathogenesis of obstructive sleep apnoea syndrome. Substantial data support the role of a local negative pressure reflex in modifying genioglossal activation across inspiration during wakefulness. Using a model of passive negative pressure ventilation, we have previously reported a tight relationship between varying intrapharyngeal negative pressures and genioglossal muscle activation (GGEMG) during wakefulness. In this study, we used this model to examine the slope of the relationship between epiglottic pressure (Pepi) and GGEMG, during stable NREM sleep and the transition from wakefulness to sleep. We found that there was a constant relationship between negative epiglottic pressure and GGEMG during both basal breathing (BB) and negative pressure ventilation (NPV) during wakefulness (slope GGEMG/Pepi 1.86+/-0.3 vs. 1.79+/-0.3 arbitrary units (a.u.) cmH2O(-1)). However, while this relationship remained stable during NREM sleep during BB, it was markedly reduced during NPV during sleep (2.27+/-0.4 vs. 0.58+/-0.1 a.u. cmH2O(-1)). This was associated with a markedly higher pharyngeal airflow resistance during sleep during NPV. At the transition from wakefulness to sleep there was also a greater reduction in peak GGEMG seen during NPV than during BB. These data suggest that while the negative pressure reflex is able to maintain GGEMG during passive NPV during wakefulness, this reflex is unable to do so during sleep. The loss of this protective mechanism during sleep suggests that an airway dependent upon such mechanisms (as in the patient with sleep apnoea) will be prone to collapse during sleep.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".