Bilateral Massive Ovarian Edema Due to Chronic Torsion Treated with Conservative Laparoscopic Approach
Bibliographic record
Abstract
Background: Massive ovarian edema (MOE) is a rare benign condition causing enlargement of the ovaries by edema fluid. The risk for women suffering from MOE is that the ovaries are removed as they can be mistaken for tumors, which can then potentially result in premature menopause. However, wedge resection and/or ovaropexy is now the treatment of choice. Case: A 28-year-old nulliparous patient experienced 8 years of episodic abdominal pain due to intermittent ovarian torsion resulting in bilateral MOE. Results: The patient was treated successfully with conservative surgery involving ovarian detorsion and bilateral ovaropexy. Her symptoms resolved and follow-up ultrasound showed a dramatic reduction in bilateral ovarian sizes. Conclusions: MOE, due to chronic torsion, can be treated with a conservative laparoscopic approach, obviating the need for oophorectomy or wedge resection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".