Assisting decision-making on age of neutering for German Short/Wirehaired Pointer, Mastiff, Newfoundland, Rhodesian Ridgeback, Siberian Husky: associated joint disorders, cancers, and urinary incontinence
Bibliographic record
Abstract
Spaying female and castrating male dogs, hereinafter referred to as neutering, is a US convention for the first year in the dog's life. Research on 35 breeds of dogs revealed that early neutering increases risks of joint disorders, such as hip dysplasia (HD), elbow dysplasia (ED), or cranial cruciate ligament (CCL) tear, or cancers, such as lymphosarcoma (LSA), mast cell tumor (MCT), hemangiosarcoma (has), or osteosarcoma (OSA), for some breeds. Joint disorder risks are heightened for some larger breeds and for mixed-breed dogs weighing more than 20 kg. Some breeds had elevated risks for cancers. Several other research teams have reported health complications associated with neutering. The study goal includes using the same methodology for data collection and analyses as in the study on 35 breeds for five additional dog breeds weighing at least 20 kg. The breeds were: German Short/Wirehaired Pointer, Mastiff, Newfoundland, Rhodesian Ridgeback, and Siberian Husky. Major differences among breeds appeared in vulnerability to joint disorders and cancers with early neutering: male and female Pointer breeds had elevated joint disorders and increased cancers; male Mastiff breeds had increased CCL and LSA and females had non-significant elevated CCL risks; female Newfoundland breeds had heightened risks for joint disorders and males had non-significant elevated risks; female Ridgeback breeds had heightened MCT with very early neutering; and Siberian Huskies showed no significant effects on joint disorders or cancers, but female breeds showed a non-significant but elevated CCL. Updated guidelines cover 40 dog breeds. These results further emphasize the importance of personalized decisions regarding the neutering of dogs, considering the dog's breed, sex, and context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".