Seroprevalence of Ehrlichia canis in dogs with suspected infection by tick-borne pathogens in Medellín, 2012-2014.
Bibliographic record
Abstract
Research is meager on canine ehrlichiosis in Colombia and it is absent in Medellín. This research aims to determine the seroprevalence of Ehrlichia canis and its distribution by sex, age, race and size in dogs diagnosed in a veterinary laboratory in Medellín, between 2012 and 2014. To the effect, a cross-sectional study was designed in 781 dogs. Overall seroprevalence of infection and specific by sex, age, size, and canine breed were calculated. In the bivariate analysis, Z test, Pearson’s chi-square test, and the Mann-Whitney U test were used. In the multivariate analysis, binary logistic regression was performed. 57 races were included, of which the most frequent were Creoles, Labradors, and French poodles; 54.9% were males, and 56.9% were adults. Overall prevalence of infection was 24.8%; highest specific seroprevalences were observed in females (25.9%), senile dogs (29.7%), and those belonging to large breeds (27.6%). The risk of infection in adult and senile dogs was two times higher than in puppies; the probability of infection was 6.4 times higher in cocker spaniels than in French bulldogs; the risk of infection in Siberian wolf, pug and Labrador was 7.8, 5.5 and 4.1 times higher than in bulldogs. High seroprevalence of canine ehrlichiosis and the identification of adult and senile dogs, and cocker spaniel, Siberian wolf, pug and Labrador breeds as of higher risk show the need to develop programs for prevention and treatment of this infection in the city.
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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 | 0.002 | 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) | 0.001 | 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".