Assessment of canine and feline body composition by veterinary health care teams in Ontario, Canada.
Bibliographic record
Abstract
Nutritional assessment guidelines recommend that veterinary teams assess the body composition of pets at every visit. The objective of this study was to determine how veterinary teams in Ontario, Canada assess body composition in cats and dogs. An online survey was distributed to veterinary teams, with questions on how often body composition is assessed, what methods are used, and demographics. The results demonstrated that 66.7% of respondents reported always assessing body composition. Of those, body condition scoring (99.4%) and body weight (99.4%) were used most often, with morphometry (41.2%) and muscle condition scoring (33.9%) used less frequently. Veterinary technicians were less likely to assess body composition compared with veterinarians. These results indicate that veterinary teams do not assess body composition as indicated by nutritional assessment guidelines. Thus, education of veterinary teams is needed, as body composition should be assessed for every patient as part of a complete nutritional assessment.
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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 | 0.000 | 0.000 |
| 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".