Owners’ attitudes and practices regarding nutrition of dogs diagnosed with cancer presenting at a referral oncology service in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: To investigate owner attitudes and dietary practices following cancer diagnosis in a dog. METHODS: A retrospective cross-sectional survey of 75 dog owners presenting with their dogs to a tertiary referral oncology service through a demographic questionnaire and in-person or telephone interviews regarding the dog's nutrition. RESULTS: Conventional diets (71%) were most commonly fed as a single diet to canine cancer patients followed by homemade cooked (7%) and homemade raw (4%). Several owners (18%) provided combinations of these diets. Owners reported some distrust towards conventional diets (51%). Appetite loss occurred in 35% of dogs and diet changes reported for 25% of dogs in the study involved exclusion of a conventional (63%) and/or inclusion of a homemade (54%) component. 90% of owners noted the diet change was associated with the cancer diagnosis. Supplements were given by 39% of owners. 85% of owners highly valued veterinary nutritional advice. CLINICAL RELEVANCE: Following a cancer diagnosis, dog owners appear to change their approach to managing their dog's nutrition. Given the value owners place on veterinary nutritional advice, veterinarians have a key role in guiding nutritional management of the canine cancer patient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".