Understanding Fetal Hemodynamics Using Cardiovascular Magnetic Resonance Imaging
Bibliographic record
Abstract
Human fetal circulatory physiology has been investigated extensively using grey-scale ultrasound, which provides excellent visualization of cardiac anatomy and function, while velocity profiles in the heart and vessels can be interrogated using Doppler. Measures of cerebral and placental vascular resistance, as well as indirect measures of intracardiac pressure obtained from the velocity waveform in the ductus venosus are routinely used to guide the management of fetal cardiovascular and placental disease. However, the characterization of some key elements of cardiovascular physiology such as vessel blood flow and the oxygen content of blood in the arteries and veins, as well as fetal oxygen delivery and consumption are not readily measured using ultrasound. To study these parameters, we have historically relied on data obtained using invasive measurements made in animal models, which are not equivalent to the human in every respect. Over recent years, a number of technical advances have been made that have allowed us to examine the human fetal circulatory system using cardiovascular magnetic resonance (CMR). The combination of vessel blood flow measurements made using cine phase contrast magnetic resonance imaging and vessel blood oxygen saturation and hematocrit measurements made using T1 and T2 mapping have enabled us to emulate those classic fetal sheep experiments defining the distribution of blood flow and oxygen transport across the fetal circulation in the human fetus. In addition, we have applied these techniques to study the relationship between abnormal fetal cardiovascular physiology and fetal development in the setting of congenital heart disease and placental insufficiency. CMR has become an important diagnostic tool in the assessment of cardiovascular physiology in the setting of postnatal cardiovascular disease, and is now being applied to the fetus to enhance our understanding of normal and abnormal fetal circulatory physiology and its impact on fetal well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
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".