Selected Contribution: Influence of genioglossus tonic activity on upper airway dynamics assessed by phrenic nerve stimulation
Bibliographic record
Abstract
Upper airway (UA) dynamics can be evaluated during wakefulness by using electrical phrenic nerve stimulation (EPNS) applied at end-expiration during exclusive nasal breathing by dissociating twitch flow and phasic activation of UA muscles. This technique can be used to quantify the influence of nonphasic electromyographic (EMG) activity on UA dynamics. UA dynamics was characterized by using EPNS when increasing tonic EMG activity with CO(2) stimulation in six normal awake subjects. Instantaneous flow, esophageal and nasopharyngeal pressures, and genioglossal EMG activity were recorded during EPNS at baseline and during CO(2) ventilatory stimulation. The proportion of twitches presenting an inspiratory-flow limitation pattern decreased from 100% at baseline to 78.7 +/- 21.4% (P = 10(-4)) during CO(2) rebreathing. During CO(2) stimuli, maximal inspiratory twitch flow (VI(max)) of flow-limited twitches significantly rose, with the driving pressure at which flow limitation occurred being more negative. For the group as a whole, the increase in VI(max) and the decrease in pressure were significantly correlated with the rise in end-expiratory EMG activity. UA stability assessed by EPNS is dramatically modified during CO(2) ventilatory stimulation. Changes in tonic genioglossus EMG activity significantly contribute to the improvement in UA stability.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".