Site of phrenic nerve stimulation-induced upper airway collapse: influence of expiratory time
Bibliographic record
Abstract
Electrical phrenic nerve stimulation (EPNS) applied at end expiration during exclusive nasal breathing can be used to characterize upper airway (UA) dynamics during wakefulness by dissociating phasic activation of UA and respiratory muscles. The UA level responsible for the EPNS-induced increase in UA resistance is unknown. The influence of the twitch expiratory timing (200 ms and 2 s) on UA resistance was studied in nine normal awake subjects by looking at instantaneous flow, esophageal and pharyngeal pressures, and genioglossal electromyogram (EMG) activity during EPNS at baseline and at -10 cmH(2)O. The majority of twitches had a flow-limited pattern. Twitches realized at 200 ms and 2 s did not differ in their maximum inspiratory flows, but esophageal pressure measured at maximum inspiratory flow was significantly less negative with late twitches (-6.6 +/- 2.7 and -5.0 +/- 3.0 cmH(2)O respectively, P = 0.04). Pharyngeal resistance was higher when twitches were realized at 2 s than at 200 ms (6.4 +/- 2.4 and 2.7 +/- 1.1 cmH(2)O x l(-1). s, respectively). EMG activity significant rose at peak esophageal pressure with a greater increase for late twitches. We conclude that twitch-induced UA collapse predominantly occurs at the pharyngeal level and that UA stability assessed by EPNS depends on the expiratory time at which twitches are performed.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".