The Contribution of MRI after Fetal Anomalies Have Been Diagnosed by Ultrasound: Correlation with Postnatal Outcomes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the additional value of fetal magnetic resonance imaging (MRI) in the assessment and management of fetuses with abnormal findings on ultrasound. METHODS: A total of 257 patients who had fetal MRI following the ultrasound diagnosis of a fetal anomaly, or were at high risk, were included. The patients were grouped by referral category for fetal MRI. Fetal MRI was compared to ultrasound in the detection of anomalies, i.e. whether additional findings were identified and if this changed diagnosis, prognosis and management during pregnancy. RESULTS: Ultrasound findings were confirmed on fetal MRI in 89% of the cases. Additional findings were seen with MRI in 28% of all patients. The diagnosis changed in 21% and the prognosis in 19% of the cases. Perinatal management changed in 8%. The antenatal findings were confirmed in all cases that had a postmortem examination following termination of pregnancy. In all the pregnancies that continued to delivery and for which the postnatal outcome is known, the findings correlated in 97% of the cases. CONCLUSION: Fetal MRI provided additional detection of fetal anomalies, leading to a change in diagnosis and prognosis in 19% of the cases. Neonatal and postmortem findings mostly confirmed the fetal MRI diagnosis, suggesting it to be a useful tool for clinical decision making in perinatal management.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".