Assessment of upper airway stabilizing forces with the use of phrenic nerve stimulation in conscious humans
Bibliographic record
Abstract
Phrenic nerve stimulation (PNS) applied at end-expiration allows the investigation of passive upper airway (UA) dynamic during wakefulness. Assuming that phasic UA dilating/stabilizing forces should modify the UA properties when twitches are applied during inspiration, we compared the UA dynamic responses to expiratory and inspiratory twitches (2 s and 200 ms after expiratory and inspiratory onset, respectively) in nine men (mean age 28 yr). This procedure was repeated with a 2-cm mouth opening provided with a closed mouthpiece. The percentage of flow-limited (FL) twitches was significantly higher when PNS was realized during expiration than during inspiration. Maximal inspiratory flow (Vi(max)) of FL twitches was significantly higher for inspiratory twitches (1,383 +/- 42 and 1,185 +/- 40 ml/s). With mouth aperture, Vi(max) decreased with an increase in the corresponding pharyngeal resistance values, and the percentage of twitch with a FL regimen increased but only for inspiratory twitches. We conclude that 1) UA dynamics are significantly influenced by the inspiratory/expiratory timing at which PNS is applied, 2) the improvement in UA dynamic properties observed from expiratory to inspiratory PNS characterizes the overall inspiratory stabilizing effects, and 3) mouth aperture alters the stability of UA structures during inspiration.
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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".