Aging in cats
Bibliographic record
Abstract
PRACTICAL RELEVANCE: Aged pets comprise a significant proportion of the small animal veterinarian's patient population; in the USA, for example, it was estimated that over 20% of pet cats were 11 years of age or older in 2011. Certain changes associated with aging are neither positive nor negative, but others are less desirable, associated with illness, changes in mobility or the development of unwanted behaviors. These changes can greatly affect the health and wellbeing of the cat and have a tremendous impact on the owner. CLINICAL CHALLENGES: Regular veterinary examinations are essential for evaluating the health of older patients and for providing owners with guidance regarding optimal care. With the exception of overt disease, however, it is difficult to definitively determine if a cat is displaying changes that are appropriate for age or if they reflect an abnormal process or condition. GOALS: This is the first of two review articles in a Special Issue devoted to feline healthy aging. The goals of the project culminating in these publications included developing a working definition for healthy aging in feline patients and identifying clinical methods that can be used to accurately classify healthy aged cats. This first review provides a thorough, systems-based overview of common health-related changes observed in cats as they age. EVIDENCE BASE: There is a paucity of research in feline aging. The authors have drawn on expert opinion and available data in both the cat and other species.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".