A survey on the prevalence of diarrhea in a Portuguese population of police working dogs
Bibliographic record
Abstract
BACKGROUND: Diarrhea is considered the most common clinical sign of chronic gastrointestinal disease in dogs and affects a considerable portion of working and sporting dogs. We aimed to determine the prevalence of diarrhea in police working dogs and evaluate the relationship between feeding, activity level, and animal characteristics with clinical signs. In an observational, prospective study, information on 188 dogs was collected. For each patient, age, sex, breed, specific mission, number of animals at the same housing location, and activity level was recorded. A body condition (BCS) and canine inflammatory bowel disease activity index (CIBDAI) scores were determined, and feces classified according to the Bristol Stool Form Scale. The Kruskal-Wallis test was used to compare recorded data between breeds, mission, age, and sex. Multiple regression was run to predict BCS score, increased defecation frequency, diarrhea, CIBDAI scores, Bristol stool scores, diarrhea from activity level, number of animals at the same housing location, breed, and mission. A p < 0.05 was set. RESULTS: Animals in the sample (male n = 96, female n = 92) had a mean age of 5.2 ± 3.2 years and a bodyweight of 24.1 ± 7.2 kg. Four main dog breeds were represented, 80 Belgian Malinois Shepherd Dogs, 52 German Shepherd Dogs, 25 Labrador Retrievers, and 19 Dutch Shepherd Dog. A prevalence of diarrhea of 10.6% was determined, with 4% of dogs having liquid diarrhea. Dogs classified as "extremely active" were more likely to have a low BCS, and the level of activity contributed to diarrhea and BCS prediction. CONCLUSION: Police working dogs frequently experience diarrhea episodes, which lead to clinical disease and performance loss. Investigation of aetiologies is required.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".