Effects of continuous positive airway pressure on upper airway inspiratory dynamics in awake patients with sleep‐disordered breathing
Bibliographic record
Abstract
Continuous positive airway pressure (CPAP) is the main treatment of the obstructive sleep apnoea syndrome (OSAS). We assessed its effects on the upper airway (UA) dynamics in response to bilateral anterior magnetic phrenic nerve stimulation (BAMPS) in 17 awake untreated OSAS patients (15 males; 52 +/- 7 years) whose effective CPAP (P(eff)) had been determined beforehand by a conventional titration sleep study. All twitch-related inspirations were flow-limited, flow first rising to a maximum (V(Imax)), then decreasing to a minimum (V(Imin)), and then increasing again (M-shaped pattern). Up to V(Imin), the relationship between driving pressure (P(d)) and flow (V) could adequately be fitted to a polynomial regression model (V = k(1)P(d) + k(2)P(d)(2); r(2) = 0.71-0.98, P < 0.0001). At atmospheric pressure V(Imax) was 700 +/- 377 ml s(-1), V(Imin) was 458 +/- 306 ml s(-1), k(1) was 154.5 +/- 63.9 ml s(-1) (cmH(2)O)(-1), and k(2) was 10.7 +/- 7.3 ml s(-1) (cmH(2)O)(-1). CPAP significantly increased V(Imax) and V(Imin) (peak values 1007 +/- 332 ml and 837 +/- 264 ml s(-1), respectively) as well as k(1) and k(2) (peak values 300.9 +/- 178.2 ml s(-1) (cmH(2)O)(-1) and 55.2 +/- 65.3 ml s(-1) (cmH(2)O)(-1), respectively). With increasing CPAP, k(1)/k(2) increased up to a peak value before decreasing. We defined as P(eff,stim) the CPAP value corresponding to the highest k(1)/k(2) value. P(eff,stim) was correlated with P(eff) (P(eff) = 7.0 +/- 2.0; P(eff,stim) = 6.4 +/- 2.6 cmH(2)O; r = 0.886; 95 % CI 0.696-0.960, P < 0.001). We conclude that CPAP improves UA dynamics in OSAS and that the therapeutic CPAP to apply can be predicted during wakefulness using BAMPS.
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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) | 0.001 | 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".