Success of Hypoglossal Nerve Stimulation Using Mandibular Advancement During Sleep Endoscopy
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Hypoglossal nerve stimulation (HGNS) effectively treats obstructive sleep apnea in select patients. Drug-induced sleep endoscopy (DISE) is required for HGNS candidacy. Data suggest that mandibular advancement (MA) devices and HGNS share similar target populations. We aimed to test the association between MA's effect on the velum and lateral walls during DISE in relation to the improvement in the apnea-hypopnea index (AHI) with HGNS. STUDY DESIGN: Prospective case series METHODS: All patients completed preoperative polysomnography or home sleep study, DISE with MA prior to HGNS implantation, and full-night efficacy sleep tests. Adult patients with body mass index (BMI) ≤ 35 and AHI ≥ 15 were included. Two independent reviewers scored DISE videos. RESULTS: . Patients with a reduced airway response to MA had greater AHI improvement than patients with a robust response (21.7, 95% confidence interval [CI]: 14.4 to 29.0 vs. 4.9, 95% CI: -8.9 to 18.6; P = .03). Patients with complete baseline collapse at the velum and lateral walls (n = 11) had less response compared to those with partial collapse (n = 35) (AHI reduction of 4.4 [95% CI: -8.6 to 17.4] vs. 22.3 [95% CI: 15.1 to 29.6; P = .02]). CONCLUSIONS: Patients having significant airway improvement in the upper pharynx with MA during DISE appear less likely to succeed with HGNS. This phenomenon might be secondary to the worsened baseline obstruction of the upper pharynx in such patients. LEVEL OF EVIDENCE: 4 Laryngoscope, 2020.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".