Abdominal Striae, Cesarean Scar, and Ultrasound Sliding Sign as Predictors of Uterine Adhesions in Women Undergoing Repeat Cesarean Section
Bibliographic record
Abstract
Background The escalating global cesarean section (CS) rate has led to a growing population undergoing repeat procedures, where postoperative adhesions are a major cause of surgical complications. Preoperative prediction of adhesions remains a significant challenge in obstetrics. This study aimed to evaluate the efficacy of non-invasive predictors - abdominal striae, previous CS scar characteristics, and the transabdominal ultrasound sliding sign - in forecasting intraoperative adhesions in women with a previous CS. Methods A prospective observational study was conducted among 155 pregnant women with at least one prior CS admitted for repeat cesarean delivery. Preoperatively, abdominal striae were graded using the Davey scoring system, and the previous CS scar was assessed using the Vancouver Scar Scale (VSS). A radiologist performed an ultrasound to determine the sliding sign. Intraoperative adhesion grading was performed using the Modified Tulandi and Lyell classification. Statistical analysis was done using Statistical Package for the Social Sciences (SPSS) version 23 (IBM Corp., Armonk, NY), with a p-value < 0.05 considered significant. Results Among the 155 participants, 68 (43.8%) had intra-abdominal adhesions, while 87 (56.2%) had none. The incidence of adhesions increased significantly with the number of previous CSs - 20 (26.7%) after one, 35 (51.5%) after two, and 13 (83.3%) after three or more CS (p = 0.007). A negative sliding sign was strongly associated with adhesions (60; 60.6%) compared to a positive sign (8; 14.3%, p < 0.001). Similarly, severe striae were linked to a higher adhesion rate (56; 62.9%, p < 0.001), and VSS >4 indicated significantly more adhesions (54; 58.1%, p < 0.001). Conclusion The study concludes that the preoperative assessment of abdominal striae, previous cesarean scar quality, and the ultrasound sliding sign provides a simple, non-invasive, and effective triad for predicting uterine adhesions. Integrating these markers into clinical practice can enhance preoperative risk stratification, optimize surgical planning, and improve patient safety during repeat CSs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".