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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".