OC25.01: Cerebral oxygen delivery, brain growth and white matter maturation are reduced in congenital heart disease fetuses
Bibliographic record
Abstract
To investigate the relationship between fetal hemodynamics and brain growth and maturation in congenital heart disease (CHD) fetuses using magnetic resonance imaging (MRI). Fetal and newborn brain MRI was performed at 37 ± 1 and 40 ± 1.5 weeks respectively on a 1.5 T scanner. Fetal hemodynamics were assessed using our previously published technique, consisting of blood flow measures and T2 based oximetry. We measured fetal combined ventricular output (CVO), umbilical vein (UV) flow, oxygen content (estimated hematocrit), oxygen delivery (DO2) and consumption (VO2). Using superior vena cava flow (QSVC) for cerebral blood flow, we measured cerebral DO2, VO2 (CDO2 & CVO2) and oxygen extraction fraction (OEF) using QSVC and the oxygen saturation (SaO2) difference in the ascending aorta and SVC. All brain volumes (BV) were calculated by segmenting a 3D brain acquisition. Newborn white matter microstructure (WMM) was examined using a diffusion tensor imaging (DTI) sequence with 12 manually placed regions of interest. 46 normal and 40 CHD fetuses were scanned, of which 35 normal and 34 CHD had newborn DTI. Gestational age and body weight was not different between groups. CHD fetal CDO2 was significantly lower due to lower QUV, UVSaO2, and poor streaming. Despite a larger fraction of DO2 directed to the fetal brain, this was associated with smaller newborn BV. CDO2 and CVO2 indexed to BV were not different between groups. However, when indexed to fetal weight, we found significant correlations between CDO2 and CVO2 and neonatal BV (both p = 0.04, r = 0.05). The lack of brain growth shown in CHD newborns was associated with abnormal WMM on DTI, but DTI was not significantly correlated with any fetal hemodynamic parameters. Our results are in keeping with a hemodynamic driver of impaired fetal brain growth typical of CHD.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".