Constructing a patient‐specific computer model of the upper airway in sleep apnea patients
Bibliographic record
Abstract
OBJECTIVE: The use of computer simulation to develop a high-fidelity model has been proposed as a novel and cost-effective alternative to help guide therapeutic intervention in sleep apnea surgery. We describe a computer model based on patient-specific anatomy of obstructive sleep apnea (OSA) subjects wherein the percentage and sites of upper airway collapse are compared to findings on drug-induced sleep endoscopy (DISE). STUDY DESIGN: Basic science computer model generation. METHODS: Three-dimensional finite element techniques were undertaken for model development in a pilot study of four OSA patients. Magnetic resonance imaging was used to capture patient anatomy and software employed to outline critical anatomical structures. A finite-element mesh was applied to the volume enclosed by each structure. Linear and hyperelastic soft-tissue properties for various subsites (tonsils, uvula, soft palate, and tongue base) were derived using an inverse finite-element technique from surgical specimens. Each model underwent computer simulation to determine the degree of displacement on various structures within the upper airway, and these findings were compared to DISE exams performed on the four study patients. RESULTS: Computer simulation predictions for percentage of airway collapse and site of maximal collapse show agreement with observed results seen on endoscopic visualization. CONCLUSION: Modeling the upper airway in OSA patients is feasible and holds promise in aiding patient-specific surgical treatment. LEVEL OF EVIDENCE: NA. Laryngoscope, 128:277-282, 2018.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".