Exposome du chien âgé en milieu urbain et décès toute cause - résultats issus de la cohorte SeniorDog
Bibliographic record
Abstract
The exposome represents the totality of exposures to environmental factors (including lifestyle) to which an organism is subjected throughout its life, from the prenatal period until death. The concept of exposome enables the recognition of risk factors in epidemiological studies. As well as being of real interest to veterinary gerontology, studying the exposome in dogs would also provide a better understanding of the human exposome. The aim of this study was to investigate the causal association between components of the exposome identified as potential risk factors for death in dogs - physical activity, diet, smoking environment and air pollution - and the occurrence of death from any cause in older dogs. SeniorDog is a cohort conducted between March 2015 and July 2020 at the Ecole nationale vétérinaire d'Alfort including 80 Labrador or Golden Retrievers of owners aged 9 years or more. At inclusion in the cohort (D0), information was collected by a questionnaire. Kaplan-Meier survival curves were traced for each component of the exposome studied, and hazard ratios (HR) for death were estimated by a Cox model. In our data, dogs with poor activity died faster than active dogs, with a median survival time of 15.0 months and 37.7 months respectively. Independently of the dog's baseline characteristics (age at D0, sex, breed, and neutering) and after controlling for other potential confounders, dogs with poor activity at D0 died significantly faster than active dogs (HR ajusted = 2.50 (1.34; 4.68), p- value < 0.01). We were unable to establish causal associations with the other components of the exposome studied and the occurrence of death from any cause : these associations were close to being significant. A methodological weakness rather than a real absence of causality seems to us the most likely explanation. Further studies are needed to clarify these results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".