Disease Prevalence and Therapeutics of Pet Dogs at Central Veterinary Hospital (CVH), Bangladesh
Bibliographic record
Abstract
Keeping dogs as pets is increasing in Bangladesh; however, they may be infected by several diseases as well as pose a serious health hazard to pet owners through the transmission of zoonotic diseases.This study aimed to investigate the disease prevalence and drug use among pet dogs at the Central Veterinary Hospital (CVH) in Bangladesh.Ninety (90) pet dogs that were brought to the CVH were the subjects of a two-month cross-sectional prospective study that took place between July and August of 2022.Patient owners' complaints, clinical disease histories, and patient clinical examinations were used to diagnose the clinical diseases.The most common clinical diseases in dogs were viral infections (55.56%), followed by bacterial infections (17.78%), other diseases (10%), fungal infections (8.89%), and parasitic infestations (7.78%) (p<0.001).Among them, canine parvovirus (28.89%) and viral fever (18.89%) were more common than other illnesses (p<0.001).Crossbreeds had the highest occurrence of clinical diseases (about 29%), followed by German shepherd breeds (24%), local breeds (20%), Labrador breeds (13%), Pug breeds (9%), and Doberman breeds (5%) (p<0.001).Male dogs had a higher percentage of clinical cases (about 69% vs. 31%) than female dogs (p<0.001).Dogs aged seven months to three years had a higher percentage of reported clinical cases (62%) than dogs of other ages (p<0.001).Breed, sex, and age all had a substantial impact on the disease categories (p<0.05).In pet dogs, ceftriaxone was the most often prescribed antibiotic.The results of this study offer interesting information about the most common diseases in pet dogs and the drugs used for the treatment of them.
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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) | 0.003 | 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".