Laparoscopic ovariectomy in guinea pigs: A pilot study
Bibliographic record
Abstract
Abstract Objective To assess the feasibility and safety of laparoscopic ovariectomy in guinea pigs utilizing 3‐mm minilaparoscopic instruments. Study design Experimental pilot study. Animals Guinea pigs (n = 3). Methods The guinea pigs were sedated, placed under general anesthesia, and intubated under endoscopic visualization. A 3‐port technique was used with a 3.9‐mm cannula for the endoscope and two 3.5‐mm cannulas accommodating 3‐mm endoscopic instruments including a 3‐mm vessel sealing device, grasping forceps, and endoscopic scissors. The abdomen was insufflated with CO2 to a pressure of 6–8 mm Hg. The guinea pigs were manually tilted 90° laterally to visualize the dorsally positioned ovaries. Results The procedure was successfully performed in all 3 animals. The surgery times were 120, 45, 45 minutes for the 3 guinea pigs, and anesthesia times were 186, 90, and 76 minutes, respectively. Placing the animals in complete lateral recumbency was found to be critical to visualize and manipulate the ovaries. The guinea pigs recovered smoothly from anesthesia. Conclusion Laparoscopic ovariectomy with 3‐mm minilaparoscopic instrumentation using a 3‐port technique was feasible in guinea pigs. Clinical significance Laparoscopic ovariectomy can be considered as an alternative to open ovariectomy as an elective surgical technique to prevent reproductive disorders in guinea pigs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".