Some Challenges and Potential Issues Regarding Cone Beam Computed Tomography of the Upper Airway
Bibliographic record
Abstract
Breathing is essential for human survival.During clinical and intraoral evaluations, dentists can identify some phenotypic characteristics that have been linked to sleep-disordered breathing (SDB).Therefore, dentists could be engaged in screening and, when indicated, refer patients with high risk for obstructive sleep apnea (OSA) to appropriate medical specialists for a full diagnosis.Participation by the dentists in future interdisciplinary management can follow.Although cone-beam computed tomography (CBCT) is an established imaging modality in dentistry, it still carries some challenges related to volume rendering management and interpretation.This article outlines the challenges of assessing available CBCT and the use of commercially available rendering tools of the upper airway.The focus of this article is on important considerations regarding the use of this advanced imaging modality for nasopharyngeal obstruction assessments and volume reconstructions.It is suggested that dentists have an essential role in SDB screening, and when CBCT is already available, it could be used as a complementary tool for volumetric airway assessment.Such involvement implies the need for a more robust understanding of current CBCT imaging limitations and potential.Finally, the role of three-dimensional printing from upper airway three-dimensional imaging for educational purposes in dentistry is discussed.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".