Laparoscopic Removal of a Large Abdominal Testicular Teratoma in a Standing Horse
Bibliographic record
Abstract
OBJECTIVE: To describe laparoscopic removal of a large testicular teratoma in a standing horse. STUDY DESIGN: Clinical report. ANIMALS: Thoroughbred horse (11 months) with a testicular teratoma. METHODS: A unilateral cryptorchid testicle could not be removed by an inguinal approach under general anesthesia because of it s large size. After recovery from general anesthesia, ultrasound evaluation revealed a 24 x 19 cm fluid-filled testicular mass. The mass was removed by paralumbar fossa laparoscopy with the horse in a standing position. After fluid aspiration of the mass, the mesorchium and ductus deferens were ligated with extracorporeal knots and the mass retrieved inside a laparoscopic specimen pouch. Morphologic features were consistent with a teratoma. RESULTS: Laparoscopic-guided aspiration of fluid from the teratoma decreased mass size and increased ease of manipulation and retrieval. Retrieval of the teratoma in a laparoscopic specimen pouch prevented loss of abdominal insufflation, helped reduce fluid leakage, and potential seeding of neoplastic cells. CONCLUSION: Use of laparoscopy for removal of neoplastic cryptorchid testicles offers many advantages including minimal invasiveness and increased safety associated with good visibility of structures. CLINICAL RELEVANCE: Standing laparoscopic surgery should be considered for removal of testicular neoplasms in horses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".