Cognitive enhancement in middle-aged and old cats with dietary supplementation with a nutrient blend containing fish oil, B vitamins, antioxidants and arginine
Bibliographic record
Abstract
Cognitive dysfunction syndrome is a major disease affecting old cats and is the consequence of severe and irreversible loss of brain cells and brain atrophy. The present study focused on the hypothesis that the optimal strategy for promoting successful brain ageing is to target risk factors associated with brain ageing and dementia. We used a nutritional strategy involving supplementation with a blend of nutrients (antioxidants, arginine, B vitamins and fish oil) to test this hypothesis. Middle-aged and old cats between 5·5 and 8·7 years of age were assigned to cognitively equivalent control or treatment groups based on prior cognitive experience and performance on baseline cognitive tests. The cats in the treatment group were maintained on a diet supplemented with the nutrient blend and the cats in the control group were maintained on the identical base diet without the additional supplementation. After an initial wash-in period, all cats were tested on a battery of cognitive test protocols. The cats fed the test diet showed significantly better performance on three of four test protocols: a protocol assessing egocentric learning, a protocol assessing discrimination and reversal learning and a protocol focused on acquisition of a spatial memory task. The results support the hypothesis that brain function of middle-aged and old cats can be improved by the nutrient blend that was selected to minimise or eliminate the risk factors associated with brain ageing and dementia.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".