Prevalence of canine parvoviral enteritis in pet dogs at Dhaka city of Bangladesh
Bibliographic record
Abstract
Background: Canine parvoviral enteritis is a highly contagious viral disease of dog that can lead to life-threating illness.Objectives: The present study was conducted to determine the prevalence of canine parvoviral enteritis in dogs of Dhaka City Corporation, Bangladesh.Methods: A total of 545 dogs were examined at Dr. Sagir’s Pet Clinics and Research Centre, Dhaka during September 2016 to August 2017. The disease was diagnosed on the basis of clinical history, clinical signs and by CPV rapid Ag kit test.Results: Overall prevalence of canine parvoviral enteritis was recorded as 13.94%. The prevalence of canine parvoviral enteritis varied significantly (p<0.05) among different aged groups (23.63%, 10.63%, 8.27% in 0-6 months, 7-12 months and above 12 months respectively). Considering seasonal influences, highest prevalence was found in summer season (17.5%) followed by winter (12.12%) and rainy season (11.66%) which was statistically insignificant (p>0.05). Male dogs (18.74%) were found to be significantly (p<0.05) higher susceptible in comparison with female (11.00%).Non-vaccinated dogs (80.0%) were at greater risk than vaccinated (2.58%), (p<0.05). There was significant (p<0.05) difference among various breeds where German Shepherd (40.78%) had highest prevalence of canine parvoviral enteritis followed by Labrador (22.36%), Rottweiler (21.05%), Doberman (13.15%) and cross breeds (4.4%). Dogs with poor health condition (20.75%) were more vulnerable than apparently healthy dogs (7.5%), (p<0.05).Conclusion: This result provides an empirical scenario of canine parvoviral enteritis in Dhaka city. Effective routine vaccination and control measures may reduce the disease burden in dog population.
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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 | 0.001 |
| 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".