Steamed hay for the prevention of severe equine asthma exacerbations
Bibliographic record
Abstract
BACKGROUND: Steaming hay reduces respirable particles and is commonly used to feed horses with asthma. However, it showed inconsistent benefits in clinical studies. OBJECTIVES: (1) To assess the effects of steamed hay on lung function and airway inflammation in horses with severe equine asthma (SEA) in remission; (2) To compare these effects with a dry hay diet. STUDY DESIGN: Cross-over in vivo experiment. METHODS: Horses were fed steamed and dry hay for 4 weeks in a prospective, cross-over study, with a 4-week washout period. Lung function, bronchoalveolar lavage (BALF) cytology, and a 23-point weighted clinical score (WCS) were recorded before and after 4 weeks of hay feeding. A mixed linear model with post hoc tests was used. RESULTS: Resistance at 5 Hz (R5) increased over the 4-week period (time effect and post hoc end vs. baseline: p < 0.001), with no difference between treatments (mean [SD], kPa/L/s) (baseline dry: 0.065 [0.014]; end dry: 0.079 [0.019]; baseline steamed: 0.063 [0.009]; end steamed: 0.078 [0.014]). There was a significant increase in BALF neutrophil percentages over time (end vs. baseline: p < 0.001) (baseline dry: 6.7 [5.4]; end dry: 13.1 [6.0]; baseline steamed: 5.6 [2.6]; end steamed: 10.5 [4.3]). WCS did not change significantly (baseline dry: 2.6 [1.5]; end dry: 2.2 [1.2]; baseline steamed: 2.9 [1.5]; end steamed: 1.9 [1.3]). MAIN LIMITATIONS: This study involves small number of horses in a research setting. Hay dust content and particles in the breathing zone were not measured. CONCLUSIONS: Steamed hay induced a mild but significant deterioration of lung function and inflammation in horses with SEA. The lack of differences with dry hay could be due to the unexpectedly mild exacerbation during this study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".