14201 The Four-Step Approach to Oophorectomy Post Hysterectomy
Bibliographic record
Abstract
Study Objective The objective of this study is to demonstrate a systematic approach to oophorectomy post hysterectomy. Design Two surgical cases and accompanying literature are shown to demonstrate the systematic approach to oophorectomy post hysterectomy. Setting Each of the surgical interventions occurred in an inpatient operating room, and both were in an elective setting. Patients or Participants Two patients consented for participation in the video. Our first patient was diagnosed with a left ovarian cyst and was seeking oophorectomy. She previously had a laparoscopic hysterectomy for adenomyosis. The second patient had BRCA2 and was seeking a risk reducing bilateral oophorectomy. She previously had an abdominal hysterectomy for heavy menstrual bleeding. Interventions Two different laparoscopic oophorectomies post hysterectomy are demonstrated. Measurements and Primary Results This video demonstrates common surgical encounters post hysterectomy. Notably the ovaries are often found in the retroperitoneum and behind bowel adhesions. To ensure safety of critical structures, and that no ovarian remnant is left behind, a systematic approach is required. This video reviews a step-wise approach to oophorectomy post hysterectomy, and demonstrates its application in a simple and more complex case. The steps are: first, perform a general survey and lysis of adhesions. Second, enter the retroperitoneum and perform ureterolysis. Third, skeletonize the infundibulopelvic ligament, seal, and transect. Fourth, excise the ovary off the pelvic side wall. Conclusion This presented surgical method to oophorectomy post hysterectomy allows for a systematic approach, helping ensure safety of critical structures and that no ovarian remnant is left behind. The approach can be applied to simple and complex cases.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".