Ovarian remnant syndrome in small animals: case series
Bibliographic record
Abstract
Our objective was to report the utility of various diagnostic tests in identifying ovarian remnant syndrome (ORS) in dogs and cats. Medical records from 2 referral teaching hospitals (Health Sciences Centre at the Ontario Veterinary College and Cornell University Hospital for Animals) were examined and 48 animals (31 dogs and 17 cats) were chosen. Data included were based on sufficient clinical or diagnostic evidence of ORS. Histopathology was used as the confirmatory test for ORS. There was no difference between the proportions of dogs versus cats diagnosed with ORS. Similarly, there was no difference in the proportions of ovarian remnants (OR) between large, medium, or small dogs, or the side (right, left, or bilateral) in which OR was diagnosed. Vaginal cytology and transabdominal ultrasonography findings, and serum progesterone concentrations had the highest chance of correctly identifying an OR prior to exploratory surgery. Transabdominal ultrasonography had strong agreement with OR location identified at surgery. Presumptive intraoperative diagnosis of OR was possible in 39/41 cases (95.1%). Auxiliary diagnostic testing should be recommended to confirm functional ovarian tissue before surgery to reduce unnecessary surgery. Additionally, transabdominal ultrasonography examination may reduce surgical time since ovarian remnant location has strong agreement with surgical findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".