Exploring the Dose–Response Relationship Between Mandibular Protrusion and Respiratory Effort Burden in Oral Appliance Therapy for Obstructive Sleep Apnea
Bibliographic record
Abstract
Abstract Rationale Increased respiratory effort (RE) is a critical feature of obstructive sleep apnea (OSA). Although prior studies have established the efficacy of mandibular advancement device (MAD) therapy in reducing the apnea–hypopnea index (AHI), the impact of MAD therapy on RE burden remains unexplored. Objectives In this study, we used a validated mandibular jaw movement (MJM) monitoring technology to determine the dose–response relationship between MAD protrusion levels and RE burden measured as the percentage of total sleep time (TST) spent in elevated respiratory effort (REMOV) during MAD titration. Methods Ninety-three patients with OSA eligible for MAD treatment were included in this prospective cohort study. A subjective titration process involved iterative adjustments based on the persistence or worsening of OSA symptoms. Optimal AHI and REMOV responses were defined as an AHI reduction of >50% and a residual REMOV <14% TST, respectively. MJM-based home sleep tests were conducted at initial, intermediate, and final protrusion levels. The treatment effect on REMOV was estimated by regression analysis. Results AHI and REMOV reductions increased progressively with higher MAD protrusion levels, with AHI decreasing by 10.3, 12.7, and 13.0 events/h and REMOV by 14.5%, 16.8%, and 18.6% TST across the three titration steps. However, a consistent discrepancy was observed between REMOV and AHI responses: at the end of titration, 68.8% of patients achieved optimal responses for both indices, whereas 15.1% had optimal REMOV response without AHI normalization, and 5.4% showed the reverse. Regression analysis showed a significant dose–response relationship for REMOV, with a 10% TST reduction within the 0–6.5 mm protrusion range and diminishing benefits beyond 6.5 mm. Of note, each millimeter advancement would yield a 2.6% TST (95% confidence interval, −3.0% to −2.1%) improvement in REMOV. Conclusions Our findings demonstrate a dose–response relationship between the MAD protrusion level and the improvement in RE burden. Optimal responses in both AHI and REMOV signify greater efficacy of MAD therapy in reducing obstructive respiratory events and RE burden. This underscores the benefit of using at-home MJM analysis to monitor these two critical metrics in the management of MAD therapy to achieve better clinical outcomes and enhance MAD titration efficacy.
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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 | 0.004 | 0.007 |
| 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 | 0.001 |
| 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".