Surgical Management for Transposed Ovarian Recurrence of Cervical Cancer: A Systematic Review with Our Experience
Bibliographic record
Abstract
In early-stage cervical cancer, ovarian metastasis is relatively rare, and ovarian transposition is often performed during surgery. Although rare, the diagnosis and surgical approach for recurrence at transposed ovaries are challenging. This study focused on the diagnosis and surgical management of transposed ovarian recurrence in cervical cancer patients. A 45-year-old premenopausal woman underwent radical hysterectomy, bilateral salpingectomy, and pelvic lymphadenectomy following postoperative concurrent chemoradiotherapy for stage IB1 cervical cancer. During the initial surgery, the ovary was transposed to the paracolic gutter, and no postoperative complications were observed. Ovarian recurrence was diagnosed using positron emission tomography-computed tomography, and a laparoscopic bilateral oophorectomy was performed. A systematic review identified nine women with transposed ovarian recurrence with no other metastases of cervical cancer, and no studies have discussed the optimal surveillance of transposed ovaries. Of those (n = 9), four women had died of the disease within 2 years of the second surgery, and the prognosis of transposed ovarian cervical cancer seemed poor. Nevertheless, three women underwent laparoscopic oophorectomies, none of whom experienced recurrence after the second surgery. Few studies have examined the surgical management of transposed ovarian recurrence. The optimal surgical approach for transposed ovarian recurrence of cervical cancer requires further investigation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".