0549 Treating OSA With Oral Appliances: A Feedback Controlled Mandibular Positioner Prospectively Identifies Responders
Bibliographic record
Abstract
We have developed a feedback controlled mandibular positioner (FCMP) incorporated into a home sleep test (HST) to predict outcome with oral appliance therapy (OAT). A previous clinical trial with prototype FCMP device demonstrated significant prospective predictive accuracy. The purpose of the present study was to assess prospective predictive accuracy of a final device (MATRx plus). Patients with OSA (n=53; mean AHI: 29.5 hr-1; mean BMI: 30.9 kg/m2) received a 2- to 3-night FCMP test in the home, and all patients received custom OA by a blinded dentist. AHI and ODI values were measured at baseline and outcome (custom OA in place) in the home using the HST component of the device. Using a success criterion of ODI < 10 hr-1, agreement analysis between the FCMP prediction and OAT outcome yielded sensitivity, specificity, positive and negative predictive values (%) for the present study of 90.5%, 90.9%, 97.4%, and 71.4%, for the previous study of 85.3%, 92.9%, 96.7%, and 72.2%, and for both studies combined of 88.2%, 90.0%, 97.3% and 71.9%, respectively. The overall accuracy of the two studies was comparable (present: 90.5%; previous: 87.5%). Use of AHI < 10 hr-1 as a success criterion was available only in the present study, and the values for the above agreement parameters were 88.1%, 90.0%, 97.3%, and 64.3%, respectively. The equivalence of the agreement parameters derived from these two independent studies suggests that the results of the FCMP test are generalizable to other OSA populations. We conclude that the final product embodying our FCMP technology provides a feasible home test that accurately predicts OAT outcome. These results also indicate that the FCMP test accurately predicts OAT outcome in an AHI framework. Zephyr Sleep Technologies, Prosomnus Sleep Technologies.
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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 | 0.000 |
| 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 | 0.001 |
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".