Feasibility of quantification of the distribution of blood flow in the normal human fetal circulation using CMR: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: We present the first phase contrast (PC) cardiovascular magnetic resonance (CMR) measurements of the distribution of blood flow in twelve late gestation human fetuses. These were obtained using a retrospective gating technique known as metric optimised gating (MOG). METHODS: A validation experiment was performed in five adult volunteers where conventional cardiac gating was compared with MOG. Linear regression and Bland Altman plots were used to compare MOG with the gold standard of conventional gating. Measurements using MOG were then made in twelve normal fetuses at a median gestational age of 37 weeks (range 30-39 weeks). Flow was measured in the major fetal vessels and indexed to the fetal weight. RESULTS: There was good correlation between the conventional gated and MOG measurements in the adult validation experiment (R=0.96). Mean flows in ml/min/kg with standard deviations in the major fetal vessels were as follows: combined ventricular output (CVO) 540 ± 101, main pulmonary artery (MPA) 327 ± 68, ascending aorta (AAo) 198 ± 38, superior vena cava (SVC) 147 ± 46, ductus arteriosus (DA) 220 ± 39,pulmonary blood flow (PBF) 106 ± 59,descending aorta (DAo) 273 ± 85, umbilical vein (UV) 160 ± 62, foramen ovale (FO)107 ± 54. Results expressed as mean percentages of the CVO with standard deviations were as follows: MPA 60 ± 4, AAo37 ± 4, SVC 28 ± 7, DA 41 ± 8, PBF 19 ± 10, DAo50 ± 12, UV 30 ± 9, FO 21 ± 12. CONCLUSION: This study demonstrates how PC CMR with MOG is a feasible technique for measuring the distribution of the normal human fetal circulation in late pregnancy. Our preliminary results are in keeping with findings from previous experimental work in fetal lambs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".