Discriminative power of phrenic twitch-induced dynamic response for diagnosis of sleep apnea during wakefulness
Bibliographic record
Abstract
The diagnosis of the obstructive sleep apnea syndrome relies on polysomnography. Bilateral anterior magnetic phrenic stimulation (BAMPS) mimics the dissociation between upper airway (UA) muscles and diaphragm commands that leads to UA closure during sleep. We evaluated BAMPS as a mean to identify obstructive sleep apnea syndrome patients through the characterization of the UA dynamics in 28 consecutive awake patients (18 apneic and 10 nonapneic). Driving pressure (Pd) and instantaneous flow (V) were recorded in response to BAMPS to determine the point of flow limitation (Vimax) and of minimal flow (Vimin) and the flow-pressure relationship [Vi = (k(1) x Pd) + (k(2) x Pd(2))]. Vimax, Vimin, UA resistance at Vi(min), and the coefficient of the flow-pressure relationship (k(1)) were correlated with apnea-hypopnea index (respectively, R = -0.735, P < 0.0001; R = -0.584, P = 0.001; R = 0.474, P = 0.01; and R = -0.567, P < 0.01). Body mass index was also correlated with apnea-hypopnea index (R = 0.500, P < 0.01). Apneic patients had a lower Vimax (Vimax = 678 +/- 386 vs. 1,247 +/- 271 ml/s; P < 0.001), a lower Vimin (Vimin = 460 +/- 313 vs. 822 +/- 393 ml/s; P < 0.05) and a lower k(1) (k(1) = 162 +/- 67 vs. 272 +/- 112 ml x cmH(2)O x s(-1); P < 0.01) than nonapneic ones. Using a classification and regression tree approach, we found that a Vimax of <803 ml/s (n = 12) selected only apneic patients. When Vimax of >803 ml/s (n = 16), a k(1) of >266.7 ml. cmH(2)O x s(-1) identified only nonapneic patients (n = 5). In 11 cases, Vimax > 803 ml/s and k(1) < 266.7 ml. cmH(2)O x s(-1). These included five nonapneic and six apneic patients. We conclude that UA dynamic properties studied with BAMPS during wakefulness significantly differ between nonapneic and apneic patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".