Evaluation of an Ipsilateral Uterine Horn Resection and Ovariectomy Surgical Model in Gilts for Embryo Collection
Bibliographic record
Abstract
Minimizing the risk of disease transmission, disseminating superior genetics, and reducing transportation costs are recognized advantages of embryo biotechnologies. These advantages make the development of a minimally invasive and repeatable procedure in pigs enticing, but simultaneously magnify the anatomical constraints. For decades, the swine industry has struggled to establish a universal procedure to collect pre-implantation embryos from pigs due to their long and convoluted uterine horns (UHs). Thus, the objectives were to evaluate the benefits of employing a transitional surgical model by shortening UH tissue using a 40 cm ipsilateral resection and assess the compensatory ovulatory response following an ovariectomy. The surgery was deemed successful as the UH was resected and the contralateral UH was fully ligated. The dam- and sire-line gilts exhibited ovarian hypertrophy between surgery and slaughter on the remaining ovary, illustrated by an increase in the number of corpora lutea (13.4 and 3.0 vs. 27.2 and 12; p < 0.05, respectively) and intact ovary weight (11.9 and 7.7 vs. 25.9 vs. 38.7 g; p < 0.05, respectively). This research is a vital step in assessing whether this interim surgical approach serves as a valuable method to advance the development of non-surgical techniques to collect pre-implantation embryos in 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".