Bibliographic record
Abstract
Spaying and castrating of dogs and cats has been considered for decades to be a routine standard of practice in veterinary medicine in the US for the prevention of numerous undesirable behaviors, medical conditions, and diseases. Additionally, the procedures have been promoted as a method of curbing the severe pet-overpopulation problem in the US. Recently, however, this routine practice has come under scrutiny and become a very controversial topic. The general wisdom and safety of the procedures have been questioned by those who are concerned that the procedures may have some unintended consequences that are only recently being recognized. The purpose of this paper is to critically examine the scientific literature regarding elective spay/castration procedures and present both risks and benefits of elective gonadectomy. After the literature is examined, it becomes clear that there may not be a single absolute optimal age to spay or castrate all dogs and cats, but that the optimal age may be dependent upon several factors, including species, breed, body size, and breed-specific diseases, among others. Determining the optimal age to perform elective gonadectomy is much clearer in cats, and the literature demonstrates that the procedures can typically be safely performed at any age after 6-8 weeks of age. The optimal age to spay or castrate dogs of certain breeds (rottweiler, golden retriever, Labrador retriever, and vizsla) is becoming less clear as studies are being conducted as to the health benefits and risks in those breeds. This review will examine these controversies and make recommendations as to the optimal age to spay/castrate dogs based upon the scientific literature.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".