Trachelectomy and Cerclage Placement as Fertility-Sparing Surgery for Cervical Cancer—An Expert Survey
Bibliographic record
Abstract
Background/Objectives: Fertility-sparing surgery (FSS) is a standard practice for managing early stage cervical cancer, yet significant variation exists in clinical approaches worldwide. Our objective was to ascertain current practices and preferences for cerclage use among expert centers globally regarding FSS in patients with early stage cervical cancer. Methods: We conducted a cross-sectional survey from May to July 2023 involving expert centers identified through their scientific contributions and participation in international workgroups and conferences.. The survey, comprising 27 questions, evaluated existing practices in FSS. Results: Out of the centers surveyed, 21 (36.2%) gynecologic oncologists responded. For tumors <2 cm, 86% of centers preferred radical trachelectomy, primarily via the vaginal approach, while 13.6% favored a simple trachelectomy. Three experts preferred simple trachelectomy (13.6%). For tumors >2 cm, 47.6% utilized neoadjuvant chemotherapy before trachelectomy. Others did not offer FSS or performed an abdominal radical trachelectomy. Over time, there has been a shift towards less radical surgeries for tumors <2 cm and increased use of neoadjuvant chemotherapy for larger tumors. Some abandoned the minimally invasive surgical approach. Nearly all experts (90.5%) placed a cerclage immediately following trachelectomy. Conclusions: The majority of experts opt for radical trachelectomy in early stage cervical cancer, with immediate cerclage placement being a common practice. However, considerable international variations highlight the urgent need for standardized guidelines and further research to optimize treatment strategies, balancing oncological safety with fertility outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".