A serological survey of tick-borne pathogens in dogs in North America and the Caribbean as assessed by <i>Anaplasma phagocytophilum, A. platys, Ehrlichia canis, E. chaffeensis, E. ewingii, and Borrelia burgdorferi</i> species-specific peptides
Bibliographic record
Abstract
INTRODUCTION: Tick-borne pathogens cause a spectrum of disease manifestations in both dogs and humans. Recognizing regional and temporal shifts in exposure are important as tick distributions change. To better delineate regional exposure to canine tick-borne pathogens, an expanded set of species-specific peptides were used to detect Anaplasma phagocytophilum (Aph), Anaplasma platys (Apl), Ehrlichia canis (Ec), Ehrlichia chaffeensis (Ech), Ehrlichia ewingii (Eew), and Borrelia burgdorferi (Bb) antibodies in canine serum. METHODS: Archived canine serum samples (n=6,582) collected during 2008-2010 and in 2012 from the US, Canada, and the Caribbean were retrospectively screened for antibodies against Ehrlichia and Anaplasma species-specific peptides. Overall, regional and temporal seroprevalence rates were determined. RESULTS: Overall Bb and Eew were the most seroprevalent pathogens. During 2008-2010, seroprevalence rates increased overall for Aph and Ech, and regionally, Bb and Aph seroprevalence rates increased in the South. Canada had unexpectedly high seroprevalence rates for Ec and Apl. The most common co-exposures were Eew+Ech, followed by Aph+Bb and Eew+Bb. CONCLUSIONS: This study demonstrated significant shifts in canine vector-borne disease seroprevalence rates. The use of specific peptides facilitated improved geographic delineation of tick-borne pathogen distributions among dogs, which may enhance epidemiological surveillance of vector-borne pathogens shared by dogs and humans.
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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 | 0.000 | 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) | 0.000 | 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".