Cesarean Scar Niche and Pelvic Pain: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Pain is a long-accepted but inadequately explored symptom of cesarean scar niche (CSN) and it is important to systematic assess the prevalence and features of pelvic pain associated with CSN, also referred to as cesarean scar defect or isthmocele. The purpose of this study is to evaluate the association between CSN and pelvic pain. DATA SOURCES: A comprehensive strategy was used to search MEDLINE, EMBASE, Pub Med, Cochrane CENTRAL, and CINAHL from database inception to February 20th, 2025. METHODS OF STUDY SELECTION: We included randomized controlled trials, prospective and retrospective cohorts, and case series involving symptomatic patients with a radiological diagnosis of CSN that evaluated pain as an outcome. Risk of bias was assessed with the Robins-I tool. The protocol was registered in PROSPERO (CRD42022346443). TABULATION, INTEGRATION AND RESULTS: The primary outcome was prevalence of pelvic pain in patients with confirmed CSN. Other outcomes included the risk of pain in patients with CSN compared those without, and changes in pain symptoms following medical or surgical management of niche. Sixty-four studies reported on pain (dysmenorrhea, dyspareunia, chronic pelvic pain (CPP), suprapubic pain (SPP)) in patients with CSN. Patients with a CSN were at increased risk of dysmenorrhea (RR: 2.25, 95% CI, 0.90-5.63), dyspareunia (RR: 2.06 95% CI, 1.42-2.99), and CPP (2.72, 95% CI, 1.63-4.54) compared to those without. In patients with confirmed niche, the prevalence of dysmenorrhea was 38.2% (95% CI 28.8-48.6); dyspareunia 28.2% (95% CI 15.3-46.0); CPP 26.8% (95% CI, 18.6-36.9) and SPP 32.5% (95% CI, 18.6-36.9). Both medical and surgical treatment of CSN significantly reduced pain symptoms (OR: 0.13, 95% CI, 0.08-0.23). Most studies were high risk for measurement bias due to lack of standardization for outcome measures. CONCLUSION: There is a strong association between CSN and various pelvic pain symptoms. Future studies require standardization of nomenclature and reporting for pain in this context.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".