Incidence of Health and Behavior Problems in Service Dog Candidates Neutered at Various Ages
Bibliographic record
Abstract
Saint Francis Service Dogs (SFSD) trains dogs to aid people with multiple sclerosis, brain injury, and many other conditions. Organizations like SFSD must carefully consider when to neuter dogs to give them the best chance at successfully completing lengthy and expensive training. The objective of this retrospective cohort study was to assess differences in the incidence of health or behavior problems leading to dismissal between dogs neutered at different ages. Data on 245 dogs-including birth date, sex, neuter date, dismissal or successful completion of training, and (where applicable) reason for dismissal-were collected from SFSD records. Age-at-neuter was grouped (<7 months; 7-11 months; > 11 months) and compared for dogs who successfully completed training and dogs who were dismissed. Dogs neutered from 7 to 11 months of age were dismissed at a significantly lower overall rate than dogs neutered at an older or younger age. There were no differences between males and females. Labrador and golden retrievers were less likely to be dismissed than other breeds. This pattern was the same for dismissals for behavioral reasons. Dogs neutered at <7 months had more than twice the risk for health-related dismissals as dogs neutered at any older age and this pattern held for orthopedic dismissals. Labradors were at higher risk for orthopedic-related dismissal than golden retrievers and all other breeds. This study suggests that there is a relationship between dogs' age at neuter and the incidence of health and behavioral problems that can lead to dismissal from service dog training.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".