Evaluating aging in cats
Bibliographic record
Abstract
PRACTICAL RELEVANCE: Many of the changes that occur with aging are not considered pathologic and do not negatively affect overall wellness or quality of life. Ruling out disease is essential, however, when attempting to determine whether an aged cat can be considered 'healthy'. A clear understanding of the normal and abnormal changes that are associated with aging in cats can help practitioners make decisions regarding medical management, feeding interventions and additional testing procedures for their aged patients. CLINICAL CHALLENGES: It can be difficult to determine if a cat is displaying changes that are appropriate for age. For example, healthy aged cats may have hematologic or serum biochemistry changes that differ from those of the general feline population. Assessment of behavioral health and cognitive changes, as well as auditory, olfactory and visual changes, can also be challenging in the aged patient. GOALS: This is the second 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 second review proposes criteria for assessing 'healthy aged cats'. EVIDENCE BASE: There is a paucity of research in feline aging. The authors draw on expert opinion and available data in both the cat and other species.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".