Polysomnographic Endotyping to Select Patients with Obstructive Sleep Apnea for Oral Appliances
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Rationale Oral appliance therapy is efficacious in many patients with obstructive sleep apnea (OSA), but prediction of treatment outcome is challenging. Small, detailed physiological studies have identified key OSA endotypic traits (pharyngeal collapsibility and loop gain) as determinants of greater oral appliance efficacy. Objectives We used a clinically applicable method to estimate OSA traits from routine polysomnography and identify an endotype-based subgroup of patients expected to show superior efficacy. Methods In 93 patients (baseline apnea–hypopnea index [AHI], ≥20 events/h), we examined whether polysomnography-estimated OSA traits (pharyngeal: collapsibility and muscle compensation; nonpharyngeal: loop gain, arousal threshold, and ventilatory response to arousal) were associated with oral appliance efficacy (percentage reduction in AHI from baseline) and could predict responses to treatment. Multivariable regression (with interactions) defined endotype-based subgroups of “predicted” responders and nonresponders (based on 50% reduction in AHI). Treatment efficacy was compared between the predicted subgroups (with cross-validation). Results Greater oral appliance efficacy was associated with favorable nonpharyngeal traits (lower loop gain, higher arousal threshold, and lower response to arousal), moderate (nonmild, nonsevere) pharyngeal collapsibility, and weaker muscle compensation (overall R 2 = 0.30; adjusted R 2 = 0.19; P = 0.003). Predicted responders (n = 54), compared with predicted nonresponders (n = 39), exhibited a greater reduction in AHI from baseline (mean [95% confidence interval], 73% [66–79] vs. 51% [38–61]; P < 0.0001) and a lower treatment AHI (8 [6–11] vs. 16 [12–20] events/h; P = 0.002). Differences persisted after adjusting for clinical covariates (including baseline AHI, body mass index, and neck circumference). Conclusions Quantifying OSA traits using clinical polysomnography can identify an endotype-based subgroup of patients that is highly responsive to oral appliance therapy. Prospective validation is warranted.
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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 | 0.002 |
| 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 it