Epidemiological Evaluation of Neuter Status, Sex, and Breed in Dogs With Cystine Uroliths
Bibliographic record
Abstract
BACKGROUND: The majority of cystine uroliths occur in intact male dogs. Androgen-dependent (Type III) cystinuria is considered the most common cause. OBJECTIVES: Identify dog breeds in which castration is likely to decrease the risk of cystine uroliths, the potential effect of delaying castration on cystine urolith formation, and urolith recurrence frequency. ANIMALS: Records of 5477 dogs with cystine uroliths and comparison groups without cystine uroliths (263 938 dogs with non-cystine uroliths and 44 491 dogs from a hospital population). METHODS: In this case-control study, odds ratios and 95% confidence intervals (CIs) were calculated to identify breeds where the proportion of intact males with cystine uroliths was higher than that of intact males without cystine uroliths. The proportions of intact males forming cystine uroliths before 12, 24, and 36 months of age were calculated. Cystine urolith recurrence rates were assessed by breed in male dogs. RESULTS: Dogs with cystine uroliths were 99% male. Across 60 breeds, the median proportion of male cystine urolith formers that were intact was 98% (range, 40%-100%). When compared with dogs without cystine uroliths, intact males were overrepresented in cystine urolith formers in all breeds except 8 (Akita, Belgian Malinois, Brussels Griffon, Cane Corso, Coonhound, Newfoundland, Scottish Terrier, and Silky Terrier). Diagnosis occurred before 36 months of age in 28% (n = 1328) of intact male cystine urolith formers. Cystine uroliths recurred in 5.0% (n = 255) of males; 81% (n = 207) were intact males. CONCLUSIONS AND CLINICAL IMPORTANCE: Androgens likely play a role in the development of cystine uroliths across many dog breeds.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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".