The effects of incremental maxillomandibular advancement surgery on airway morphology: a cadaveric study
Bibliographic record
Abstract
Evidence demonstrates efficacy of maxillomandibular advancement (MMA) treatment of obstructive sleep apnea (OSA) and airway expansion. Patient studies are limited to pre/post-surgery comparisons. This cadaveric study evaluated intra-individual relationships between magnitudes of MMA advancement and airway changes. MMA with distraction osteogenesis devices and incremental advancement of the maxillomandibular complex, was performed on cadavers (n = 5). Computed tomography at each 2-mm advancement was used to measure volume and dimension of the oropharyngeal airway. Three-dimensional shape analysis visualized magnitudes and locations of changes. Incremental advancements caused volume, anteroposterior, and lateral dimensions to increase progressively, while length decreased. Changes were significant at lower advancements. Comparisons of MMA indicate alterations in airway volume from 4 to 6 mm and 6 to 8 mm were relatively greater than the changes from 8 to 10 mm (P = 0.044, P = 0.028, respectively), 10 to 12 mm (P = 0.024, P = 0.023), and 12 to 14 mm (P = 0.021, P = 0.019). These results may expand MMA application suggesting 6-8 mm advancements provide substantial increases in airway volume. MMA may be an OSA treatment option when large advancements are not possible. Lower magnitudes of advancement decrease risks of unfavorable facial esthetics from excess protrusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".