Gonadectomy status and age are associated with variable risk of overweight or obese outcomes in 15 dog breeds: a retrospective cohort study using data from primary care veterinary clinics
Bibliographic record
Abstract
Objective: To examine rates of overweight or obese (OvOb) body condition score, including the association between OvOb and gonadectomy, in 15 dog breeds. Methods: The analysis considered the 5 most recorded large breeds (26,369 dogs) and 10 most recorded toy/small breeds (90,002 dogs) in Banfield Pet Hospital's database from 2013 to 2019. Cox proportional hazards models evaluated associations between OvOb and gonadectomy status, gonadectomy age, sex, and primary breed. Models estimated OvOb rates in gonadectomized versus intact dogs of each breed and, separately, OvOb rates according to gonadectomy age. Results: There was substantial breed variation in underlying (intact dog) OvOb rates among the 15 breeds. Pugs, Golden Retrievers, and Labrador Retrievers had highest underlying susceptibility to OvOb outcomes. There was some variation in relative OvOb rates among breeds, but breeds differing substantially from size group peers were limited. Among all toy/small breeds, gonadectomy at 3 or 6 months had hazard ratios (relative risks) lower than, or not statistically different from, gonadectomy at 1 year or older. For large dogs, OvOb outcomes associated with prepubertal gonadectomy varied by breed. Conclusions: Underlying susceptibility to OvOb varies by breed. Gonadectomy offers significant benefits at individual and population levels. As with many veterinary care decisions, however, there is complexity, and associated OvOb risks are not uniform across breeds. Clinical Relevance: Results may facilitate more individualized recommendations for gonadectomy timing and proactive strategies (specifically diet and exercise) to mitigate risk of OvOb outcomes, while accounting for the broader context of individual dog and population-level benefits of gonadectomy.
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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 | 0.001 |
| 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 | 0.000 |
| 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".