Identification of spayed and neutered cats and dogs: Veterinary training and compliance with practice guidelines
Bibliographic record
Abstract
Spay/neuter identification tattoos and ear-tipping are simple and cost-effective methods to minimize the likelihood of unnecessary anesthesia and surgery in companion animals or the misidentification of sexually intact animals. This study assessed training of sterilization identifiers in US and Canadian veterinary schools and practitioner compliance with guidelines for identifiers via surveys conducted in 2019. Faculty in all 34 schools responded to the survey, reporting that curricula included sterilization identifiers in 31% of lecture-based training, 75% of spay/neuter laboratory-based training, and 38% of clinical practice-based training. A total of 425 facilities performing spay/neuter reported frequency and technical aspects of sterilization identifiers in client-owned and unowned (shelter, rescue, trap-neuter-return) animals. Facilities encountering large numbers of animals of unknown background, performing a high number of surgeries, or with specialized spay/neuter training were significantly more likely to use identifiers. Only 5% of private practices tattooed all owned animals, and 21% tattooed all unowned animals. In contrast, 80% of shelters and 72% of spay/neuter clinics tattooed all owned animals, and 84% of shelters and 70% of spay/neuter clinics tattooed all unowned animals. Green was the most common tattoo color (97%); the most common placement was near or in the incision for female cats (99%), female dogs (99%), and male dogs (92%), and ventral abdomen in male cats (55%). Enhanced training and implementation of best practices described in professional guidelines for sterilization identifiers are needed throughout the veterinary industry to protect animals from unnecessary procedures and to prevent unintended litters in animals misidentified as previously sterilized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".