Incidence rates and risk factor analyses for owner reported vomiting and diarrhoea in Labrador Retrievers – findings from the Dogslife Cohort
Bibliographic record
Abstract
Dogslife collects data directly from owners of Labrador Retrievers across the UK including information regarding signs of illness irrespective of whether the signs precipitated a veterinary visit. In December 2015, the cohort comprised 6084 dogs aged up to six years and their owners had made 2687 and 2601 reports of diarrhoea and vomiting respectively. The co-occurrence of vomiting and diarrhoea with other signs was described and the frequencies and durations of the two signs were examined with reference to veterinary visitation. Age-specific illness rates were described and Cox Proportional Hazards models were used to estimate risk factors. Just 37% of diarrhoea reports were associated with a veterinary visit and the proportion was even lower for vomiting at 28%; indicating that studies of veterinary practice data miss the majority of signs of gastrointestinal upset. In terms of frequency and duration, diarrhoea typically needed to last two days before the dog would be taken to the vet but if the dog vomited at least every six hours, the owner would be more likely to take the dog to the vet after one day. The illness rates of both signs peaked when the dogs were aged between three and six months. There was also a seasonal pattern to the incidents with the lowest hazards for both in May. Diarrhoea incidents peaked in August-September each year but, while vomiting appeared to be higher in September, it peaked in February. Having another dog in the household was associated with a lower hazard for both vomiting and diarrhoea but having a cat was only associated with a reduced hazard of vomiting. In addition to the distinct seasonal patterns of reporting, there were clear differences in the geographic risks for the two signs. The hazard of diarrhoea was positively associated with human population density within Great Britain (according to home post code) whereas no significant geographical association was found with vomiting. This study is particularly relevant for dog owners because it highlights the wealth of gastrointestinal illnesses in dogs that are dealt with by owners but never seen by veterinarians. The risk factor analyses make use of owner-reported demographic information, highlighting the differences between vomiting and diarrhoea. The analyses give rise to the possibility that the presence of other pets in households may affect rates of illness and indicate new avenues for investigations of these distinct, and oft-suffered conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".