Caractérisation d'un phénotype de fragilité canine
Bibliographic record
Abstract
Frailty is a geriatric syndrome of interest in the prevention of pathological aging. Its evaluation in clinical practice allows the identification of individuals at risk of negative health events. The aim of this study was to adapt the concept of frailty, developped in human geriatrics, to the dog by characterizing a frailty phenotype, using physical performance tests and showing its association with death. SeniorDog is a prospective cohort conducted between March 2015 and July 2020 at the Ecole Nationale Vétérinaire d'Alfort including dogs of owners, of Labrador or Golden Retrievers breed, aged 9 years or older. At inclusion in the cohort (D0), 80 dogs underwent physical performance tests and numerous variables were collected. Kaplan-Meier survival curves were plotted according to frailty at D0, and hazard ratios (HR) of death were estimated by a Cox model, based on data collected during my 5 years of follow-up. In our data, frail dogs died faster than pre-frail and no frail dogs, with a median survival time of 10.5 months, 35.4 months, and 42.5 months, respectively. Regardless of baseline dog characteristics (age at D0, sex, breed, and sex-sterilization interaction), frail dogs at D0 died significantly faster than no frail dogs at D0 (HR adjusted = 5.86 [2.45; 14.0], (overall p-value < 0.01). This association persisted after controlling for other potential confounders. Frailty, assessed by a 5-component phenotype, appears predictive of death, in senior Goldens and Labradors Retrievers living in a family setting. To our knowledge, this is the first prospective cohort to characterize a canine frailty phenotype based on physical performance tests. The concept of frailty seems to be adaptable to the dog and thus seems to be a geriatric syndrome of interest in the management of functional aging of the dog, in the same way as for humans. Further studies are needed to generalize these results to other breeds of 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".