Cats in Positive Energy Balance Have Lower Rates of Adipose Gain When Fed Diets Containing 188 versus 121 ppm L-Carnitine
Bibliographic record
Abstract
L-carnitine (LC) is included in select adult feline diets for weight management. This study investigated whether feeding adult cats with diets containing either 188 ppm of LC (LC188) or 121 ppm of LC (LC121) and feeding them 120% of maintenance energy requirement (MER) resulted in differences in total energy expenditure (EE), metabolic fuel selection, BW, body composition, and behavior. Cats (n = 20, 4 ± 1.2 yrs) were stratified for BCS and randomly assigned to one of two dietary treatments and fed for 16 weeks. BW was measured weekly, and indirect calorimetry, body composition, physical activity, play motivation, and cognition were measured at baseline and throughout the study. A mixed, repeated measures, ANCOVA model was used. Cats in both treatments gained BW (P < 0.05) throughout the study, with no differences between treatments at any time point (P > 0.05). There were no differences in body composition between groups at baseline; however, body fat (g) and body fat : lean mass ratio were greater in cats fed LC121 in contrast to cats fed LC188 (P < 0.05) on week 16. No other outcomes differed between treatments (P > 0.05). Supplying dietary LC at a dose of at least 188 ppm may be beneficial for the health and well-being of cats fed above MER.
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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 | 0.000 |
| 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) | 0.002 | 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".