Risk assessment of sleeping disorder breathing based on upper airway centerline evaluation
Bibliographic record
Abstract
One of the most important breathing disorders in childhood is obstructive sleep apnea syndrome which affects 2–3% of children, and the reported failure rate of surgical treatment was as high as 54%. A possible reason in respiratory complications is having reduced dimensions of the upper airway which are further compressed when muscle tone is decreased during sleep. In this study, we use Cone-beam computed tomography (CBCT) to assess the location or cause of the airway obstruction. To date, all studies analyzing the upper airway in subjects with Sleeping Disorder Breathing were based on linear, area, or volumetric measurements, which are global computations and can easily ignore local significance. Skeletonization was initially introduced as a 3D modeling technique by which representative medial points of a model are extracted to generate centerlines for evaluations. Although centerlines have been commonly used in guiding surgical procedures, our novelty lies in comparing its geometric properties before and after surgeries. We apply 3D data refinement, registration and projection steps to quantify and localize the geometric deviation in target airway regions. Through cross validation with corresponding subjects’ therapy data, we expect to quantify the tolerance threshold beyond which reduced dimensions of the upper airway are not clinically significant. The ultimate goal is to utilize this threshold to identify patients at risk of complications. Outcome from this research will also help establish a predictive model for training and to estimate treatment success based on airway measurements prior to intervention. Preliminary results demonstrate the feasibility of our approach.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".