Evaluation of condylar and mandibular movements on the upper airway during the use of mandibular advancement device for obstructive sleep apnea treatment
Bibliographic record
Abstract
Abstract Objectives: To evaluate the temporomandibular joint (TMJ), condylar and mandibular movements in obstructive sleep apnea (OSA) patients treated with mandibular advancement device (MAD), and to identify the influence of these anatomic factors on upper airway (UA) volume and polysomnographic outcomes after treatment. Materials and methods: Twenty OSA patients were prospectively treated with MAD. Clinical examinations, cone-beam computed tomography, and polysomnography were performed before MAD treatment and after achieving therapeutic protrusion. Polysomnographic variables and three-dimensional measurements of the TMJ, mandible, and upper airway were statistically analyzed. Results: Condylar rotation, anterior translation, and anterior mandibular displacement were directly correlated with total UA volume, while vertical mandibular translation was inversely correlated with the volume of the inferior oropharynx. MAD treatment resulted in an increase in the volume and area of the superior oropharynx. There was no statistically significant correlation between condylar rotation and translation and polysomnographic variables. With MAD, there was a significant increase in vertical dimension, changes in condylar position (rotation and translation), and mandibular displacement. The central and medial lengths of the articular eminence were inversely correlated with condylar rotation and translation, respectively. The lateral length of the eminence was directly correlated with condylar translation, and the lateral height was directly correlated with condylar rotation and translation. Conclusion: Condylar and mandibular movements influenced UA volume. The articular eminence played a role in the amount of condylar rotation and translation. Clinical relevance: Individualized anatomical evaluation of the TMJ proves to be important in the therapy of OSA with MAD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".