Owner perception of health of North American dogs fed meat- or plant-based diets
Bibliographic record
Abstract
BACKGROUND: Some dog owners elect to feed their dog a plant-based food either as part of or for their entire dietary intake. Being omnivores or facultative carnivores, a strictly plant-based diet is not the natural type of food dogs evolved to consume, leaving some question as to whether this feeding management strategy is safe and healthy for dogs. OBJECTIVES: This study surveyed owner perceptions of health and wellbeing of dogs and compared between those fed meat-based and plant-based diets. METHODS: A web-based questionnaire was distributed to pet owners to collect data on dog characteristics, husbandry, health and wellbeing. Univariate comparisons between diet groups was made by chi square analyses or Kaplan-Meier tests as appropriate, with a significance cut-off value of 0.05. Multivariate models were negative binomial and logistic regression for count and categorical data, respectively. RESULTS: Owners feeding plant-based diets to their dog reported fewer health disorders, specifically with respect to ocular or gastrointestinal and hepatic disorders. Dog longevity was reported to be greater for dogs fed plant-based diets. Owners feeding plant-based diets to their dogs relied less on veterinary associates for nutrition information, versus dog owners feeding meat-based diets. CONCLUSIONS: Dog owners feeding a plant-based diet did not perceive adverse health effects in their dogs. The results might suggest an association between feeding a plant-based diet and perceived health and longevity, however inherent bias and limitations associated with surveys of owner perception must be considered, and objective research is required to determine if plant-based diets truly affect canine health.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".