Unconventional diets and nutritional supplements are more common in dogs with cancer compared to healthy dogs: An online global survey of 345 dog owners
Bibliographic record
Abstract
This survey aimed to investigate and compare diet type and supplement use between dogs (Canis lupus familiaris, L.) with cancer and a population of owner-reported healthy dogs and to assess the sources of information dog owners consult. Respondents were mainly from English-speaking countries. Dogs were considered healthy (N = 213) if owners reported them to be in good health. Dogs were included in the cancer group (N = 132) if the owner reported that their dog had been diagnosed with cancer. An online survey was distributed to clients presenting to a tertiary oncology service, clients presenting to a local primary care veterinary practice, and through social media. Owners of dogs with cancer spent more time researching pet health (P < .001), pet nutrition (P < .01) and nutritional supplements (P < .001) than owners of healthy dogs. While veterinarians were most commonly reported to be an information source for both groups, owners of healthy dogs more likely consulted pet stores and owners of dogs with cancer tended more to social media groups and blogs. Healthy dogs were more likely fed commercial dry food (P < .001), whereas homemade cooked (P < .001) and raw diets (P < .05) were more prevalent among dogs with cancer. Supplement use, especially cannabidiol products, mushroom extracts or turmeric/curcumin, was also more common for this group (P < .001). Alternative diets and supplements were more popular among owners of dogs with cancer compared to owners of healthy dogs. These findings highlight the need for nutritional counselling and education of pet owners regarding nutrition-related topics, especially when their dog is diagnosed with cancer.
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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 | 0.001 | 0.001 |
| 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".