Computed tomographic geometrical analysis of surgical treatments for equine recurrent laryngeal neuropathy
Bibliographic record
Abstract
OBJECTIVES: To characterize the 3-D geometry of the equine larynx replicating laryngeal hemiplegia and 4 surgical interventions by use of CT under steady-state airflow conditions. Secondly, to use fluid mechanic principles of flow through a constriction to establish the relationship between measured airflow geometries with impedance for each surgical procedure. SAMPLE: 10 cadaveric horse larynges. PROCEDURES: While CT scans were performed, inhalation during exercise conditions was replicated for each of the following 5 conditions: laryngeal hemiplegia, left laryngoplasty with ventriculocordectomy, left laryngoplasty with ipsilateral ventriculocordectomy and arytenoid corniculectomy, corniculectomy, and partial arytenoidectomy for each larynx while CT scans were performed. Laryngeal impedance was calculated, and selected cross-sectional areas were measured along each larynx for each test. Measured areas and constriction characteristics were analyzed with respect to impedance using a multilevel, mixed-effects model. RESULTS: Incident angle, entrance coefficient, outlet coefficient, friction coefficient, orifice thickness, and surgical procedure were significantly associated with upper airway impedance in the bivariable model. The multivariate model showed a significant influence of incident angle, entrance coefficient, and surgical procedure on impedance; however, the orifice thickness became nonsignificant within the model. CLINICAL RELEVANCE: Laryngeal impedance was significantly associated with the entrance configuration for each procedure. This suggested that the equine upper airway, despite having a highly complex geometry, adheres to fluid dynamic principles applying to constrictions within pipe flow. These underlying flow characteristics may explain the clinical outcomes observed in some patients, and lead to areas of improvement in the treatment of obstructive upper airway disease in horses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".