Interpretation of Wave Reflections in the Umbilical Arterial Segment of the Feto-Placental Circulation: Computational Modeling of the Feto-Placental Arterial Tree
Bibliographic record
Abstract
Placental vascular abnormalities are associated with a host of pregnancy complications including placenta mediated fetal growth restriction (FGR). Umbilical arterial (UA) Doppler ultrasound velocity waveforms are widely used in the diagnosis of underlying placental vascular abnormalities in pregnancies with suspected FGR, which greatly helps to prevent stillbirth via ongoing fetal monitoring and timely delivery. However, the sensitivity of UA Doppler diagnosis diminishes late in gestation. Our goal was to present a generalized wave decomposition method to compute forward and reflected components from UA waveforms. A detailed anatomical based model was also developed to explain observed UA flow waveform and to explore how vascular properties affect the shape of flow wave components. Using data from a previous study of pregnant mice using high frequency ultrasound microscopy, we examined in utero Doppler and M-mode ultrasound measurements in 15 fetuses' UA. Following ultrasound, the placentas had been collected and perfused with contrast agent to obtain high-resolution 3D images of the feto-placental arteries. Model of these specimens indicates the significant role of terminal load impedance (capillary and/or veins) in creating positive or negative reflected flow waveforms. A negative flow reflected waveform is obtained when terminal impedance is elevated. This is consistent with the elongated and non-branching terminal villi that are proposed to cause the highly abnormal UA waveforms found in early-onset FGR. The significance of these findings for the diagnostic utility of UA Doppler in human pregnancy is that the identification and measurement of wave reflections may aid in discriminating between healthy and abnormal placental vasculature in pregnancies with suspected late-onset FGR.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".