Cine-MRI for Quantifying Uterine Peristalsis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Uterine contractility, also known as uterine peristalsis (UP), is a critical determinant of fertility, affecting sperm transport and embryo implantation. Increased uterine peristaltic activity has been associated with reduced pregnancy rates. However, data are heterogeneous and uterine contractility has not been widely translated into clinical practice. Cine-MRI, although limited by cost and heterogeneity in data reporting, has emerged as a promising tool to assess uterine dynamics and increase our knowledge of UP in physiological and pathological conditions. Objective: This systematic review and meta-analysis aimed to describe patterns of UP in physiological and pathological uterine conditions, including endometriosis and fibroids, using cine-MRI. Methods: A systematic literature search of the Medline, Embase, Cochrane and CENTRAL databases and Google Scholar was conducted up to May 2024, including studies evaluating UP by cine-MRI. Clinical studies evaluating uterine contractility were included, excluding those affected by therapeutic interventions or unrelated pathologies. This meta-analysis pooled data from studies comparing uterine contractility in patients with endometriosis. Results: In the 13 included studies (365 women), uterine contractility varied significantly according to menstrual cycle phases and pathological conditions. This meta-analysis showed that women with endometriosis had higher uterine contractility in the luteal phase (0.74; 95% CI: 0.27–1.21) but not in the periovulatory phase (SMD 0.8; 95% CI: −3.78–5.37). Conclusions: Cine-MRI is a promising diagnostic tool for the analysis of UP. Endometriosis is associated with impaired UP, which may be a cause of the decreased implantation rate and infertility in endometriosis. However, further research is needed to consolidate the effect of UP on implantation and fertility and to develop standardised and cost-effective tools to assess uterine contractility and tailor infertility treatment.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".