Impact on life expectancy was the most important information to clients when considering whether to take action for an overweight or obese dog
Bibliographic record
Abstract
OBJECTIVE: To determine dog owner preferences for information communicated during veterinarian-client obesity-related conversations within companion animal practice. SAMPLE: Dog owners recruited using snowball sampling. METHODS: A cross-sectional online questionnaire was distributed to dog owners. A discrete choice experiment was used to determine the relative importance, to participating dog owners, of information about selected weight-related attributes that would encourage them to pursue weight management for a dog when diagnosed as overweight by a veterinarian. RESULTS: A total of 1,108 surveys were analyzed, with most participating dog owners residing in Canada. The most important weight-related attribute was life expectancy (relative importance, 28.56%), followed by the timeline for developing arthritis (19.24%), future quality of life (18.91%), change to cost of food (18.90%), and future mobility (14.34%). CLINICAL RELEVANCE: Results suggest that dog owners may consider information relating to an extension of their dog's life as the most important aspect of an obesity-related veterinary recommendation. By integrating dog owner preferences into discussions between clients and veterinary professionals about obesity, there is the potential to encourage more clients to engage in weight management efforts for their overweight or obese dog.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".