Population characteristics of golden retriever lifetime study enrollees
Bibliographic record
Abstract
BACKGROUND: Studying cancer and other diseases poses a problem due to their protracted and multifactorial nature. Prospective studies are useful to investigate chronic disease processes since collection of lifestyle information, exposure data and co-incident health issues are collected before the condition manifests. The Golden Retriever Lifetime Study is one of the first prospective studies following privately-owned dogs throughout life to investigate the incidence and risk factors for disease outcomes, especially cancer.Owners of golden retrievers in the contiguous United States volunteered their dogs in early life. Owners and veterinarians complete online questionnaires about health status and lifestyle; dogs undergo a physical examination and collection of biological samples annually. The data presented summarize the initial study visits and the corresponding questionnaires for 3044 dogs in the cohort. RESULTS: The median age of dogs at enrollment was 14.0 months (interquartile range (IQR): 8-20 months). Approximately half of the population had undergone gonadectomy by their initial study visit. Medical conditions reported at enrollment consisted primarily of integumentary, gastrointestinal and urinary dysfunction. A large majority of the dogs have a record of having received preventive care (vaccines, parasiticides, flea and heartworm prevention) by the time of the initial study visit. Clinical pathology data were unremarkable. CONCLUSIONS: This study represents one of the first lifetime observational investigations in veterinary medicine. The population characteristics reported here indicate a healthy cohort of golden retrievers cared for by owners committed to their dogs' health. Data acquired over the study period will provide valuable information about genetic, dietary and environmental risk factors associated with disease in golden retrievers and a framework for future prospective studies in veterinary medicine.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".