Magnetic Resonance Imaging: A New Tool to Optimize the Prediction of Fetal Anemia?
Bibliographic record
Abstract
INTRODUCTION: The false-positive rate in the prediction of fetal anemia is 10-15%. We investigated if a new, noninvasive MRI method used as a supplement to ultrasound could improve the prediction. METHODS: Fetuses suspected of anemia and controls were scanned in a 1.5-tesla MRI scanner 1-4 times during pregnancy. Cases were scanned before and after intrauterine blood transfusion with a T1-mapping MRI sequence in a cross-section of the umbilical vein. RESULTS: Inclusion of 8 cases and 11 controls resulted in 10 case scans (2 cases were included twice) and 33 control scans. In controls, the T1 relaxation time was 1,005-1,391 ms; in cases with severe anemia, 1,505-1,595 ms, moderate anemia 1,503-1,525 ms, and no/mild anemia 1,245-1,410 ms. After blood transfusions, values dropped to 1,123-1,288 ms. The mean value in moderate and severe anemic cases was 275 ms higher than in controls (95% CI 210-341 ms, p < 0.0001), and after blood transfusion it was comparable to controls (3 ms, 95% CI -62 to 68 ms, p = 0.934). A 1,450-ms cut-off would have identified all cases in need of blood transfusion with no false-positive cases. CONCLUSIONS: Our findings indicate a potential for this new MRI method to improve the prediction of fetal anemia as a supplement to ultrasound.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".