Canine Model of Human Frailty: Adaptation of a Frailty Phenotype in Older Dogs
Bibliographic record
Abstract
Frailty is a clinical presentation resulting from age-related cumulative declines in several physiological systems. The aim of this study was to adapt the concept of frailty to the domestic dog, as a model for frailty research, by characterizing a 5-criterion frailty phenotype using objective measurement, and to investigate its independent association with death. A prospective cohort including 80 Labrador and Golden Retriever dogs aged 9 years or older was conducted between March 2015 and July 2020. An adapted frailty phenotype was defined according to the presence of 5 criteria (weakness, slowness, poor endurance, low physical activity, and shrinking) evaluated at baseline from physical performance tests and items from questionnaire and physical examination. Survival analysis was used to investigate the association between frailty status and time to all-cause death over 5 years of follow-up. Frailty status was significantly associated with all-cause death, with median survival times of 10.5 months, 35.4 months, and 42.5 months, respectively for dogs with 3 or more criteria (frail dogs), dogs with 1 or 2 criteria (prefrail dogs), and nonfrail dogs. Independently of age, sex, breed, sterilization, and sex-sterilization interaction, frail dogs died significantly faster than nonfrail dogs at baseline (adjusted hazard ratio = 5.86; 95% confidence interval = 2.45-14.0; p < .01). This significant association persisted after controlling for other potential confounders. Frailty, assessed by a 5-criterion phenotype, was predictive of all-cause death, in geriatric Labrador and Golden Retriever dogs. The concept of frailty seems adaptable to the dog.
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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 | 0.001 |
| 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".