Potential Drivers for the Re-Emergence of Canine Leptospirosis in the United States and Canada
Bibliographic record
Abstract
Canine leptospirosis is an important zoonotic disease in many countries. This review examines potential drivers for increased diagnoses of canine leptospirosis in the United States and Canada, using the epidemiologic triad of agent-environment-host as a template. Leptospira spp. are classified into more than 250 serovars, but in many laboratories only 6 are routinely tested for in serologic agglutination tests of canine sera. Leptospiral infections in dogs may potentially go undetected with unemployed or currently employed diagnostic methods. Disease transmission from infected reservoir hosts usually occurs via urine-contaminated environmental sources such as water. Direct contact between infected and susceptible individuals, environmental factors such as climate changes in temperature and/or rainfall, and increasing number and urbanization of reservoir hosts may greatly increase dog exposure risks. A dog’s lifestyle may influence exposure risk to leptospirosis, but vaccination based on proper identification of circulating serogroups dramatically reduces post-exposure infections. Regrettably, resistance to vaccination by veterinarians and dog owners leaves a large number of dogs at risk for this zoonotic disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".