T2* Placental Magnetic Resonance Imaging in Preterm Preeclampsia
Bibliographic record
Abstract
Placental dysfunction underlies the cause of pregnancies complicated by preeclampsia. The use of placental magnetic resonance imaging to provide an insight into the pathophysiology of preeclampsia and thus assess its potential use to inform prognosis and clinical management was explored. In this prospective observational cohort study, 14 women with preterm preeclampsia and 48 gestation-matched controls using 3-Tesla magnetic resonance imaging at median of 31.6 weeks (interquartile range [IQR], 28.6–34.6) and 32.2 weeks (IQR, 28.6–33.8), respectively, were imaged. The acquired data included T2-weighted images and T2* maps of the placenta, the latter an indicative measure of placental oxygenation. Placentae in women with preeclampsia demonstrated advanced lobulation, varied lobule sizes, high granularity, and substantial areas of low-signal intensity on T2-weighted imaging, with reduced entire placental mean T2* values for gestational age (2 sample t test, t=7.49) correlating with a reduction in maternal PlGF (placental growth factor) concentrations (Spearman rank correlation coefficient 0.76) and increased lacunarity values (t=3.26). Median mean T2* reduced from 67 ms (IQR, 54–73) at 26.0 to 29.8 weeks’ gestation to 38 ms (IQR, 28–40) at 34.0 to 37.9 weeks’ gestation in the control group. In women with preeclampsia, median T2* was 23 ms (IQR, 20–23) at 26.0 to 29.8 weeks’ gestation and remained low (22 ms [IQR, 20–26] at 34.0–37.8 weeks’ gestation). Histological features of maternal vascular malperfusion were only found in placentae from women with preeclampsia. Placental volume did not differ between the control group and women with preeclampsia. Placental magnetic resonance imaging allows both objective quantification of placental function in vivo and elucidation of the complex mechanisms underlying preeclampsia development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".