Energy intake recommendations from cat food labels sold in Ontario, Canada, diverge from predictive equations for adult cat maintenance
Bibliographic record
Abstract
Objective: To compare the recommended energy intake based on the feeding guidelines of adult maintenance cat foods sold in Canada with commonly used predictive energy equations. Methods: This cross-sectional observational study surveyed feeding guidelines on cat food labels in Guelph, ON, Canada, between July and October of 2023. Recommended energy intakes (REIs) for hypothetical 3- and 5-kg cats (low, medium, and high REIs) were calculated from label-suggested portions. Predicted reference energy values were calculated with the National Research Council equation for lean cats and American Animal Hospital Association equations for inactive/obese-prone and neutered cats. Feeding recommendation differences were analyzed by diet type, measuring unit, and label claims with nonparametric statistical methods. Results: Among 790 diets with a feeding guideline, 57% and 32% of the low REI values were below the result of the equation for inactive/obese-prone cats for 3 kg and 5 kg, respectively. In contrast, 35% (3 kg) and 52% (5 kg) of the high REI values exceeded the result of the equation for lean cats. The high REI was higher for both body sizes in all-life-stage diets compared to adult maintenance diets. The low REI for 5-kg cats was lower in weight-management diets. The medium REI was positively correlated with metabolizable energy per serving unit. Conclusions: Feeding guidelines on cat food labels in Canada frequently differed from predicted energy requirements, with discrepancies influenced by product type, feeding unit, and label claims. Clinical Relevance: Cat food feeding guidelines are a starting point; feeding amounts must be reassessed and adjusted over time based on each cat's body weight and condition trends and lifestyle.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".