Effect of poll flexion and dynamic laryngeal collapse on tracheal pressure in Norwegian Coldblooded Trotter racehorses
Bibliographic record
Abstract
REASON FOR PERFORMING STUDY: Dynamic laryngeal collapse (DLC) associated with poll flexion is a newly diagnosed upper respiratory tract obstructive disorder that causes poor racing performance. OBJECTIVES: To determine if Norwegian Coldblooded Trotters (NCTs) affected with DLC associated with poll flexion differ from normal, elite NCTs based on simple airway mechanics measurements. METHODS: Five normal elite NCTs and 6 NCTs diagnosed previously with DLC underwent treadmill videoendoscopy while tracheal pressures were measured continuously. Alternating head positions were used such that horses were exercised with free head carriage and induced poll flexion at heart rates >200 beats/min. RESULTS: Peak inspiratory tracheal pressures were significantly more negative for horses with DLC compared to the elite horses. This difference was only significant during the exercise phases when the poll region was flexed, P = 0.0015. Head position significantly affected peak inspiratory pressure for both elite and affected horses, P < 0.0001. CONCLUSIONS AND CLINICAL RELEVANCE: Induced poll flexion significantly affected peak inspiratory pressure (PIP) in all horses; however, PIPs were significantly more negative in those affected with DLC. Based upon the tracheal pressure measurements recorded in this study, DLC in NCTs is a severe obstructive upper respiratory tract disorder that is induced by poll flexion.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".