Single Incision, Laparoscopic‐Assisted Ovariohysterectomy for Mucometra and Pyometra in Dogs
Bibliographic record
Abstract
OBJECTIVE: To describe a single-incision, laparoscopic-assisted technique for ovariohysterectomy and its application for treatment of mucometra and pyometra in dogs. STUDY DESIGN: Prospective case series. ANIMALS: Seven dogs. METHODS: Dogs were included if they had an open or closed pyometra or mucometra and an approximate uterine body diameter of less than 5 cm based on ultrasound or abdominal radiographs. Each dog underwent a laparoscopic-assisted ovariohysterectomy through a single-incision laparoscopic port. RESULTS: The procedure was performed in 6 dogs with pyometra and 1 dog with mucometra. Conversion to an open procedure was necessary in 1 dog with uterine rupture. A 2nd port was necessary in 1 dog to exteriorize the uterine body. Median uterine body diameter was 2.2 cm (range 2-3.9). The median surgical time was 85 minutes (range 40-110). Six of 7 dogs were released from the hospital at 1 day postoperative. Follow up ranged from 7 to 421 days and no complications were reported. CONCLUSION: A single-incision, laparoscopic-assisted technique for pyometra was feasible in dogs, given restricted case selection and experience with single-incision laparoscopy.
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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) | 0.000 | 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 | 0.000 |
| 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".