Risk factors associated to endoparasites in dogs and cats at Prince Edward Island (Canada)
Bibliographic record
Abstract
INTRODUCTION. Although many studies on the frequency of endoparasites in dogs and cats in Canada have been reported (Joffe et al., 2011, Can. Vet. J. 52:1323-1328), seasonal and/or annual patterns are often not extimated, furthermore very few and aged papers have been written about owned dogs and cats in Canada. MATERIALS AND METHODS. The frequency of endoparasitc infections from samples of cats (2,391) and dogs (15,016) submitted to the Veterinary Teaching Hospital (VHT) of the Atlantic Veterinary College, University of Prince Edward Island-Canada was determined, using univariate and multivariate analysis. Predictors of endoparasitism, such as sex, age, geographical origin and seasonality, were also investigated through the calculation of odds ratios (OR) with 95% confidence intervals. RESULTS AND CONCLUSIONS. Overall thirteen parasite genuses were detected, cats showed a higher frequency, with this species difference being statistically significant (χ2=15.494; P<0.001). The most frequent genuses recovered were Giardia spp. (5.23%), followed by Isospora spp. (3.31%) and Toxocara spp. (3.21%). Monoparasitism was the most common in both dogs and cats, at 87.5% and 86.7%, respectively. Frequency of Giardia spp. was significantly higher (χ2=8.79; P=0.03) in the dogs during fall, as well as Toxocara spp. (χ2=48.5; P<0.001) and Isospora spp. (χ2=31.13; P<0.001). Cats more likely of being Isospora spp. positive in summer (χ2=31.27; P<0.001). Increasing the age was a protective factor in terms of parasite presence (OR=0.232; 95%CI=0.174-0.311), as well as being sterilized male (OR=0.624; 95%CI:0.419-0.931) or female (OR=0.627; 95%CI:0.419-0.938), furthermore the trend across the years showed a decreasing (OR=0.961; 95%CI: 0.931-0.991). The apparent low frequency of endoparasites should not be interpreted too rigidly, due to the fact that our population came from a Veterinary Teaching Hospital, so some selection biases should be taken into account. For example, owners who take their pets to the veterinary clinic are more likely to follow a deworming protocol than those who do not. This study shows how the diagnosis of routine fecal examinations can be investigated, providing an appreciation of risk factors most commonly associated with endoparasitism. Future research will help to evaluate if the owners’ attitudes affects the probability of parasites in pets.
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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) | 0.002 | 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".