Prediction of oral appliance efficiency in patients with apnoea using phrenic nerve stimulation while awake
Bibliographic record
Abstract
BACKGROUND: In patients with sleep apnoea, primary oropharyngeal collapse of the upper airway during sleep is an important predictor of treatment success with an oral appliance. Phrenic nerve stimulation is able to induce upper airway obstruction and was therefore used to mimic the target for an efficient treatment with an oral appliance. OBJECTIVE: To test if the site(s) of upper airway collapse identified by means of bilateral anterior magnetic stimulation during wakefulness could predict the efficacy of treatment with an oral appliance in patients with obstructive sleep apnoea. METHODS: The site(s) of upper airway collapse while awake were identified by examining the flow-pressure relationship of flow-limited twitches when measuring velopharyngeal and oropharyngeal pressure. Once the mandibular advancement titration had been completed, the efficacy of mandibular advancement was documented during an in-lab sleep study. RESULTS: 33 patients (24 men and 9 women, apnoea-hypopnoea index (AHI) 32.5 ± 17.1/h) participated in the study. Flow limitation was obtained in 29, but 3 of these had no follow-up sleep study with the device. Subjects with oropharyngeal and velopharyngeal collapse did not differ in the phenotypic characteristics associated with a positive response to an oral appliance (gender, apnoea severity, body mass index or positional dependency of breathing disturbances). Complete or partial success was seen in 14/17 subjects with twitch-induced oropharyngeal collapse and in 4/12 patients with velopharyngeal closure. Treatment response was significantly different in subjects with twitch-induced oropharyngeal and velopharyngeal collapse (OR 9.5, 95% CI 1.6 to 52.7). CONCLUSIONS: Identifying the site of upper airway collapse by using bilateral anterior magnetic stimulation of the phrenic nerve during wakefulness can predict treatment success with an oral appliance in patients with sleep apnoea.
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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 | 0.001 | 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) | 0.001 | 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".