Assessment of Upper Airway Dynamics in Awake Patients with Sleep Apnea Using Phrenic Nerve Stimulation
Bibliographic record
Abstract
Phrenic nerve stimulation can reproduce during wakefulness the dissociation between upper airway and inspiratory muscles that is associated with obstructive sleep-related breathing disorders. This could provide a useful management tool in the study of passive upper airway (UA) dynamics during wakefulness in patients with the obstructive sleep apnea-hypopnea syndrome (OSAHS). To assess the feasibility of the technique in this setting, we studied the dynamics of diaphragm twitch-associated inspiratory flow in eight patients with OSAHS. Cervical magnetic stimulation (CMS) and bilateral anterior magnetic phrenic stimulation (BAMPS) were applied at end-expiration during exclusive nasal breathing. Electrical phrenic nerve stimulation (ES) proved not feasible. The driving pressure and the respiratory resistance at peak twitch esophageal pressure obtained at maximal stimulation intensity were significantly higher with BAMPS than with CMS. A twitch-flow limitation pattern was observed in seven of eight subjects; VI(max) values of flow-limited twitches obtained at 100% stimulation intensity was 0.81 +/- 0.5 L/s with BAMPS and 0.87 +/- 0.5 L/s with CMS (p = 0.4). The number of flow-limited BAMPS twitches dropped from an average 77.5% to 18.4% with nasal continuous positive airway pressure (CPAP) levels corresponding to the patient's home treatment. We conclude that (1) BAMPS is potentially a useful tool to evaluate the dynamics of flow through the passive UA in awake OSAHS patients, (2) BAMPS may be superior to CMS in evaluating UA properties in OSAHS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".