Surgical treatment of Cesarean scar ectopic pregnancy: efficacy and safety of ultrasound‐guided suction curettage
Bibliographic record
Abstract
OBJECTIVES: To assess the efficacy of ultrasound-guided suction curettage for management of pregnancies implanted into the lower uterine segment Cesarean section scar. METHODS: This was a retrospective study including women diagnosed with Cesarean section scar pregnancy at two large tertiary referral early pregnancy units between 1997 and 2014. Surgical evacuation was offered to selected women presenting in the first trimester ≤ 14 weeks' gestation. All procedures were performed transcervically under ultrasound guidance using suction curettage. A modified Shirodkar cervical suture was used in women who required additional measures to secure hemostasis. RESULTS: A total of 232 women with Cesarean section scar pregnancy were seen at the referral units; 191/232 (82.3%) women were treated surgically. The median intraoperative blood loss was 100 mL (range, 10-3000 mL); 9/191 (4.7% (95% CI, 1.7-7.7%)) women required blood transfusion and, in one (0.5% (95% CI, 0-1.5%)), life-saving hysterectomy had to be performed because of uncontrollable intraoperative bleeding. Of the women who attended for follow-up, 7/116 (6.0% (95% CI, 1.7-10.3%)) required a repeat surgical procedure because of retained products of conception. Multivariable analysis showed that the gestational sac diameter (odds ratio (OR), 1.10 (95% CI, 1.03-1.17)) and pregnancy vascularity on Doppler examination (OR, 3.41 (95% CI, 1.39-8.33)) were significant predictors of heavy intraoperative blood loss (> 1000 mL). CONCLUSIONS: Ultrasound-guided suction curettage is an effective method for the treatment of pregnancies implanted into a lower uterine segment Cesarean section scar and is associated with a low risk of blood transfusion and hysterectomy. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.
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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 | 0.012 |
| 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".