Relationships between equine airway reactivity measured by flowmetric plethysmography and specific indicators of airway inflammation in horses with suspected inflammatory airway disease
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: Agreement between airway reactivity measured by flowmetric plethysmography and histamine bronchoprovocation, and lower airway inflammation measured by bronchoalveolar lavage (BAL) cytology, has not been studied in horses with suspected inflammatory airway disease (IAD). OBJECTIVES: We tested the hypothesis that airway reactivity is associated with BAL cytology in horses presenting for unexplained poor performance and/or chronic cough. STUDY DESIGN: Prospective clinical study. METHODS: Forty-five horses, predominantly young Standardbred racehorses, presenting for unexplained poor performance or chronic cough, underwent endoscopic evaluation, tracheal wash, flowmetric plethysmography with histamine bronchoprovocation and BAL. Histamine response was measured by calculating PC35, the concentration of nebulised histamine eliciting an increase in Δflow of 35%. RESULTS: In this population, there was no significant correlation between histamine response and cell populations in BAL cytology. When airway hyperreactivity (AHR) was defined as ≥35% increase in Δflow at a histamine concentration of <6 mg/ml, 24 of the 45 horses (53%) were determined to have AHR. Thirty-three (73%) had either abnormal BAL cytology or AHR, and were diagnosed with IAD on this basis. Of horses diagnosed with IAD, 9 (27%) had an abnormal BAL, 11 (33%) had AHR and 13 (39%) had both. CONCLUSIONS: Airway reactivity and BAL cytology did not show concordance in this population of horses presenting for unexplained poor performance and/or chronic cough. Failure to include tests of airway reactivity may lead to underdiagnosis of IAD in young Standardbred racehorses that present with clinical signs suggestive of IAD.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".