Magnetic resonance imaging in late pregnancy to improve labour and delivery outcomes – a systematic literature review
Bibliographic record
Abstract
Abstract Background Magnetic resonance imaging (MRI) provides excellent soft tissue visualisation which may be useful in late pregnancy to predict labour outcome and maternal/neonatal birth trauma. Objective To study if MRI in late pregnancy can predict maternal and neonatal outcomes of labour and birth. Methods Systematic review of studies that performed MRI in late pregnancy or immediately postpartum. Studies were included if they imaged maternal pelvic or neonatal structures and assessed birth outcome. Meta-analysis was not performed due to the heterogeneity of studies. Results Eighteen studies were selected. Twelve studies explored the value of MRI pelvimetry measurement and its utility to predict cephalopelvic disproportion (CPD) and vaginal breech birth. Four explored cervical imaging in predicting time interval to birth. Two imaged women in active labour and assessed mouldability of the fetal skull. No marker of CPD had both high sensitivity and specificity for predicting labour outcome. The fetal pelvic index yielded sensitivities between 59 and 60%, and specificities between 34 to 64%. Similarly, although the sensitivity of the cephalopelvic disproportion index in predicting labour outcome was high (85%), specificity was only 56%. In women with breech presentation, MRI was demonstrated to reduce the rates of emergency caesarean section from 35 to 19%, and allowed better selection of vaginal breech birth. Live birth studies showed that the fetal head undergoes a substantial degree of moulding and deformation during cephalic vaginal birth, which is not considered during pelvimetry. There are conflicting studies on the role of MRI in cervical imaging and predicting time interval to birth. Conclusion MRI is a promising imaging modality to assess aspects of CPD, yet no current marker of CPD accurately predicts labour outcome. With advances in MRI, it is hoped that novel methods can be developed to better identify individuals at risk of obstructed or pathological labour. Its role in exploring fetal head moulding as a marker of CPD should be further explored.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".