Use of Cognitive Testing, Questionnaires, and Plasma Biomarkers to Quantify Cognitive Impairment in an Aging Pet Dog Population
Bibliographic record
Abstract
BACKGROUND: Aging dogs may suffer from canine cognitive dysfunction syndrome (CCDS), a condition in which cognitive decline is associated with amyloid pathology and cortical atrophy. Presumptive diagnosis is made through physical examination, exclusion of systemic/metabolic conditions, and completion of screening questionnaires by owners. OBJECTIVE: This study aimed to determine whether cognitive function could be quantified in aging pet dogs, and to correlate cognitive testing with validated questionnaires and plasma neurofilament light chain (pNfL) concentration. METHODS: Thirty-nine dogs from fifteen breeds were recruited (9.3 to 15.3 years). Owners completed the Canine Dementia Scale (CADES) and Canine Cognitive Dysfunction Rating scale (CCDR). Executive control and social cues were tested, and pNfL was measured with single molecule array assay. Comparisons were made between cognitive testing scores, CADES, CCDR scores, and pNfL. RESULTS: CADES scoring classified five dogs as severe CCDS, six as moderate, ten as mild, and eighteen as normal. CCDR identified seven dogs at risk of CCDS and thirty-two as normal. Cognitive testing was possible in the majority of dogs, although severely affected dogs were unable to learn tasks. CADES score correlated with sustained attention duration (r = -0.47, p = 0.002), inhibitory control (r = -0.51, p = 0.002), detour (r = -0.43, p = 0.001), and pNfL (r = 0.41, p = 0.025). Concentration of pNfL correlated with inhibitory control (r = -0.7, p≤0.001). The CCDR scale correlated with performance on inhibitory control (r = -0.46, p = 0.005). CONCLUSION: Our findings suggest that a multi-dimensional approach using a combination of questionnaires, specific cognitive tests, and pNfL concentration can be used to quantify cognitive decline in aging pet dogs.
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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 | 0.002 |
| 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 | 0.000 |
| 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".