Seroprevalence and evaluation of risk factors associated with seropositivity for <i>Borrelia burgdorferi</i> in Ontario horses
Bibliographic record
Abstract
BACKGROUND: Recently, the blacklegged tick (Ixodes scapularis), which is the vector of Borrelia burgdorferi, has undergone a range expansion from the northeastern and mid-west United States to areas of southeastern Canada, including parts of Ontario. Understanding the seroprevalence of antibodies against B. burgdorferi in horses and risk factors for exposure is important for monitoring and preventing this emerging disease. STUDY DESIGN: Cross-sectional study of 551 horses in southern, central, and eastern Ontario, Canada. OBJECTIVES: To assess the seroprevalence of B. burgdorferi in horses in Ontario, Canada; to evaluate risk factors associated with seropositivity; and, to compare the performance of two diagnostic tests. METHODS: Plus test, IDEXX Laboratories). RESULTS: The seroprevalence of B. burgdorferi on at least one test was 17% (91/551), though only 15 (16%) horses tested positive with both tests. A spatial cluster of cases was detected in Eastern Ontario. The odds of being seropositive for B. burgdorferi on the C6 ELISA were significantly increased when oak trees were present by pastures (OR = 7.3 (1.8-29.2), P = .005), while the odds were significantly decreased when regular tick checks were performed (OR = 0.1 (0.01-0.7), P = 0.02). MAIN LIMITATIONS: Recruitment focused on known areas with blacklegged ticks as well as areas of higher horse density, which may have led to selection bias. CONCLUSIONS: The expansion of blacklegged tick populations poses an ongoing risk for horses. Assessment of diagnostic testing options and risk factors is important for diagnosis and prevention, and with further investigation this information may be used to propose changes in management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".