Scars that speak: my experience and evidence on uterine niche repair, fertility, and the hidden legacy of cesarean section — a systematic review in the Saudi context
Bibliographic record
Abstract
Abstract Background Caesarean-section (CS) rates in Saudi Arabia are relatively high, and the downstream scars may form a uterine “niche” (caesarean-scar defect) linked to abnormal bleeding, pelvic discomfort, and impaired fertility. Objective To synthesise evidence on (1) the relationship between uterine niche and fertility and (2) the effect of niche repair on reproductive outcomes, with implications for the Saudi community. Methods A PRISMA-guided search of PubMed, Embase, Scopus and Cochrane Library (through September 2025) identified studies reporting fertility or obstetric outcomes in women with a niche and after surgical repair (hysteroscopic, laparoscopic or transvaginal). Data were extracted on pregnancy, live-birth, miscarriage, residual myometrial thickness (RMT), and complications. Quality was assessed with Newcastle–Ottawa and Cochrane RoB tools. Results Eighteen studies met inclusion criteria (1 RCT, 5 prospective cohorts, 12 retrospective series). Niche prevalence after CS ranged 24–70% by TVUS and 56–84% by sonohysterography. Niche presence was associated with delayed conception and lower implantation/live-birth rates in some IVF cohorts. Surgical repair improved symptoms and, in pooled analyses, increased spontaneous pregnancy (relative risk ≈ 2.4 vs. non-treated), though heterogeneity and bias limit certainty. Saudi-specific data remain scarce, but national statistics confirm high CS utilisation. Conclusions In the Saudi context, uterine niche is a meaningful, under-recognised driver of subfertility after CS. Repair appears promising for selected symptomatic infertile women, pending larger controlled trials with long-term follow-up.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".