Numerical simulation of the effect of geometric parameters on silicone airway stent migration
Bibliographic record
Abstract
Recently, endotracheal stenting has become critical in treating respiratory diseases. Due to the COVID-19 pandemic in recent years, many patients had stenosis because of long-term intubation, and silicone stents can be used to treat tracheal stenosis in these patients. Standard airway stents are silicone tubes that provide immediate relief but are prone to migration. In this work, we design different silicone stents and analyze them in the trachea to evaluate silicone airway stents’ performance to overcome undesired migration. A finite-element model of the trachea was employed to evaluate anti-migration forces in each stent. The geometry of the trachea is brought from a computerized tomography scan of the chest of a 68-year-old healthy man. The results are shown based on the least migration of stents based on anti-migration forces. Also, the conditions of stent placement have been considered based on two different assumed friction factors, and the importance of choosing the type of silicone for stent construction has been analyzed. The results show that increasing the diameter of the stent reduces the displacement and migration of it in the trachea. Furthermore, the 23 mm stent with a 45° angle revealed the best implementation against compression under the impact of respiratory pressure differences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".