0513 Assessing precision oral appliance efficacy using frequency- and risk-based indices
Bibliographic record
Abstract
Abstract Introduction Apnea-hypopnea index (AHI), a frequency-based index commonly used to define the severity of obstructive sleep apnea (OSA), fails to capture the risk related to OSA. Sleep apnea-specific hypoxic burden (SASHB) appears to be more predictive of OSA-related risk than AHI but has only been studied in cohorts of untreated individuals. This is the first assessment of the ability of precision oral appliance therapy (OAT) to reduce SASHB and the use of SASHB, rather than AHI, to define therapeutic efficacy. Methods Data previously obtained from a clinical study investigating prediction of response to precision OAT in OSA were analyzed. Study participants with OSA (n = 11 mild, n = 19 moderate, and n = 18 severe) completed two-night level 3 home sleep tests before and after receiving a precision oral appliance (ProSomnus Sleep Technologies, Pleasanton, CA). Apnea-hypopnea index and SASHB were calculated. For SASHB, a cut-off of 53 %min/h was selected based on data suggesting that values above this limit are associated with OSA-related risk; for AHI, cut-offs of < 10 h-1 and < 15 h-1 were selected due to their widespread use in clinical practice. Results Precision oral appliance therapy reduced AHI from 31.6 ± 19.2 h-1 to 11.7 ± 15.8 h-1 (p < 0.001) and SASHB from 83.2 ± 63.6 %min/h to 25.2 ± 42.3 %min/h (p < 0.001). Using an AHI-based definition of therapeutic efficacy, 83% (for AHI < 15 h-1) and 71% (for AHI < 10 h-1) of participants achieved efficacy with precision OAT. However, when the risk-based SASHB definition of therapeutic efficacy (SASHB < 53 %min/h) was used, precision OAT efficacy increased to 90%. Conclusion In the population studied, precision OAT significantly improved SASHB. The use of AHI, a frequency-based index of OSA, appears to misclassify some individuals as therapeutic non-responders to precision OAT despite their having an SASHB in the low-risk range. Sleep apnea-specific hypoxic burden likely provides a more meaningful assessment of OSA treatment efficacy as it accounts for the risk associated with the disease. Support (if any) This work was supported by ProSomnus Sleep Technologies.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".