Computational fluid dynamic analysis of upper airway procedures in equine larynges
Bibliographic record
Abstract
Introduction Computational fluid dynamics (CFD) has proven useful in the planning of upper airway surgery in humans, where it is used to anticipate the influence of the surgical procedures on post-operative airflow. This technology has only been reported twice in an equine model, with a limited scope of airflow mechanics situations examined. The reported study sought to widen this application to the variety of procedures used to treat equine recurrent laryngeal neuropathy (RLN). The first objective of this study was to generate a CFD model of an ex-vivo box model of ten different equine larynges replicating RLN and four therapeutic surgeries to compare the calculated impedance between these procedures for each larynx. The second objective was to determine the accuracy between a CFD model and measured airflow characteristics in equine larynges. The last objective was to explore the anatomic distribution of changes in pressure, velocity, and turbulent kinetic energy associated with the disease (RLN) and each surgical procedure performed. Methods Ten equine cadaveric larynges underwent inhalation airflow testing in an instrumented box while undergoing a concurrent computed tomographic (CT) exam. The pressure upstream and downstream (outlet) were measured simultaneously. CT image segmentation was performed to generate stereolithography files, which underwent CFD analysis using the experimentally measured outlet pressure. The ranked procedural order and calculated laryngeal impedance were compared to the experimentally obtained values. Results and discussion The CFD model agreed with the measured results in predicting the procedure resulting in the lowest post-operative impedance in 9/10 larynges. Numerically, the CFD calculated laryngeal impedance was approximately 0.7 times that of the measured calculation. Low pressure and high velocity were observed around regions of tissue protrusion within the lumen of the larynx. RLN, the corniculectomy and partial arytenoidectomy surgical procedures exhibited low pressure troughs and high velocity peaks compared to the laryngoplasty and combined laryngoplasty/corniculectomy procedures. CFD modeling of the equine larynx reliably calculated the lowest impedance of the different surgical procedures. Future development of the CFD technique to this application may improve numerical accuracy and is recommended prior to consideration for use in patients.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".