No differences in IVF pregnancy outcomes following hysteroscopic polypectomy using a manual hysteroscopic tissue removal device versus conventional resection
Bibliographic record
Abstract
BACKGROUND: To compare in-vitro fertilization and embryo transfer (IVF-ET) pregnancy outcomes following endometrial polypectomy using either a manual mechanical hysteroscopic tissue resection device (mHTR) or traditional mechanical resection with scissors or polyp graspers resection. METHODS: This retrospective study examined 189 infertile patients who had undergone polypectomy prior to IVF-ET at Reproductive and Genetic Hospital of CITIC-XIANGYA. Patients undergoing polypectomy using manual mHTR resection were compared to those having the procedure through traditional mechanical resection. The primary outcome was clinical pregnancy rate (CPR) following the first ET after hysteroscopic polypectomy. Secondary outcomes included miscarriage rates and the optimal time interval from polyp resection to ET. RESULTS: One-hundred eleven (58.7%) patients underwent polypectomy via manual mHTR while 78 (41.3%) patients underwent traditional mechanical resection for polypectomy. Following the first ET, the positive pregnancy rate (80.2% vs. 79.5%; p = 1.000), CPR (70.3% vs. 74.4%; p = 0.652), and MR (11.5% vs. 12.1%; p = 1.000) were similar between those undergoing mHTR and traditional mechanical resection. After stratifying by the number of menstrual cycles from procedure (after next menses; 2 to 3 menstrual cycles; and > 3 menstrual cycles later), there was no differences in CPR when comparing the time from polypectomy and resection type. CONCLUSION: This study suggests no differences in IVF pregnancy outcomes following hysteroscopic polypectomy regardless of surgical type using manual mHTR or traditional mechanical resection and the timing from resection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".