Mandibular advancement and obstructive sleep apnoea: a method for determining effective mandibular protrusion
Bibliographic record
Abstract
The objectives of the study were to test the hypotheses that it is possible, during routine polysomnography (PSG), to prospectively identify favourable candidates for mandibular repositioning appliance (MRA) therapy in the treatment of obstructive sleep apnoea (OSA) and to accurately estimate an optimal protrusive distance at which to fabricate the MRA. A series of subjects underwent a remotely controlled mandibular positioner (RCMP) test during PSG monitoring. The ability of the RCMP test to eliminate OSA and the target protrusion at which that occurred was compared with the success of a custom oral MRA in the 33 subjects who completed the protocol. The RCMP test was a success in 15 subjects and a failure in 18 subjects. Appliance therapy was initiated in 38 subjects and completed in 33. MRA therapy was successful at target protrusion in 80% of subjects who had a successful RCMP test and failed in 78% of those who failed the RCMP test. In conclusion the remotely controlled mandibular positioner test outcome demonstrated a statistically significant association with mandibular repositioning appliance outcome. The target protrusion determined during the remotely controlled mandibular positioner test was the effective therapeutic protrusion in subjects with a successful remotely controlled mandibular positioner test.
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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 | 0.004 |
| 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 | 0.001 | 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".