The impact of caesarean scar niche on fertility - a systematic review
Bibliographic record
Abstract
BACKGROUND: The trend of increasing caesarean section (CS) rates brings up questions related to subfertility. Research regarding the influence of CS on assisted reproduction techniques (ART) is conflicting. A potential mechanism behind CS-induced subfertility is intra uterine fluid resulting from a caesarean scar defect or niche. The vaginal microbiome has been repeatedly connected to negative ART outcomes, but it is unknown if the microbiome is changed in relation to a niche. METHODS: This systematic review describes literature investigating the effect of a niche on live birth rates after assisted reproduction. Furthermore, studies investigating a difference in microbial composition in subfertile persons with a niche compared to no niche are evaluated. Pubmed, Embase and Web of Science were searched on March 2023 for comparative studies on both study questions. Inclusion criteria were i.e., English language, human-only studies, availability of the full article and presence of comparative pregnancy data on a niche. The quality of the included studies and their risk of bias were assessed using the Newcastle-Ottawa scale for cohort studies. The results were graphically displayed in a forest plot. RESULTS: Six retrospective cohort studies could be included on fertility outcomes, with a total of 1083 persons with a niche and 3987 without a niche. The overall direction of effect shows a negative impact of a niche on the live birth rate (pooled aOR 0.58, 95% CI 0.48-0.69) with low-grade evidence. Three studies comparing the microbiome between persons with and without a CS could be identified. CONCLUSION: There is low-grade evidence to conclude that the presence of a niche reduces live birth rates when compared to persons without a niche. The theory that a caesarean has a negative impact on pregnancy outcomes because of dysbiosis promoted by the niche is interesting, but there is no sufficient literature about this.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".