Risk factors for disease associated with influenza virus infections during three epidemics in horses
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors associated with respiratory tract disease in horses during 3 epidemics caused by influenza virus infections. DESIGN: Cross-sectional and prospective longitudinal observational studies. ANIMALS: 1,163 horses stabled at a Thoroughbred racetrack. PROCEDURES: Investigations were conducted during a 3-year period. An epidemic of respiratory tract disease caused by influenza virus infections was identified in each year. Routine observations and physical examinations were used to classify horses' disease status. Data were analyzed to identify factors associated with development of disease. RESULTS: Results were quite similar among the epidemics. Concentrations of serum antibodies against influenza virus and age were strongly associated with risk of disease; young horses and those with low antibody concentrations had the highest risk of disease. Calculation of population attributable fractions suggested that respiratory tract disease would have been prevented in 25% of affected horses of all horses had high serum antibody concentrations prior to exposure. However, recent history of vaccination was not associated with reduction in disease risk. Exercise ponies had greater risk of disease than racehorses, which was likely attributable to frequent horse-to-horse contact. CONCLUSIONS AND CLINICAL RELEVANCE: Particular attention should be paid to young horses, those with low serum antibody concentrations, and horses that have frequent contact with other horses when designing and implementing control programs for respiratory tract disease caused by influenza virus infections. It appears that control programs should not rely on the efficacy of commercial vaccines to substantially reduce the risk of disease caused by influenza virus infections.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".