Developmental uterine anomalies in cats and dogs undergoing elective ovariohysterectomy
Bibliographic record
Abstract
OBJECTIVE: To describe the characteristics and frequency of gross uterine anomalies in cats and dogs undergoing elective ovariohysterectomy. DESIGN: Prospective and retrospective case series. ANIMALS: 53,258 cats and 32,660 dogs undergoing elective ovariohysterectomy at 26 clinics in the United States and Canada during 2007. PROCEDURES: Clinics prospectively reported gross anomalies and submitted tissues from abnormal reproductive tracts identified during surgery. Records from a feral cat spay-neuter clinic were evaluated retrospectively. RESULTS: Suspected congenital anomalies of the uterus were identified in 0.09% (49/53,258) of female cats and 0.05% (15/32,660) of female dogs. Uterine anomalies identified included unicornuate uterus (33 cats and 11 dogs), segmental agenesis of 1 uterine horn (15 cats and 3 dogs), and uterine horn hypoplasia (1 cat and 1 dog). Ipsilateral renal agenesis was present in 29.4% (10/34) of cats and 50.0% (6/12) of dogs with uterine anomalies in which kidneys were evaluated. Mummified ectopic fetuses were identified in 4 cats with uterine anomalies. Both ovaries and both uterine tubes were present in most animals with uterine anomalies. CONCLUSIONS AND CLINICAL RELEVANCE: Urogenital anomalies were twice as common in cats as in dogs. Identification of uterine developmental anomalies in dogs and cats should trigger evaluation of both kidneys and both ovaries because ipsilateral renal agenesis is common, but both ovaries are likely to be present and should be removed during ovariohysterectomy.
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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 | 0.003 |
| 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 | 0.001 |
| 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".