Quantification of Wave Reflection in the Human Umbilical Artery From Asynchronous Doppler Ultrasound Measurements
Bibliographic record
Abstract
Elevated umbilical artery pulsatility is a widely used biomarker for placental pathology leading to intra-uterine growth restriction and, in severe cases, still-birth. It has been hypothesized that placental pathology modifies umbilical artery pulsatility by altering the degree to which the pulse pressure wave, which originates from the fetal heart, is reflected from the placental vasculature to interfere with the incident wave. Here we present a method for estimating the reflected pulse wave in the umbilical artery of human fetuses using asynchronously acquired Doppler ultrasound measurements from the two ends of the umbilical cord. This approach assumes non-dispersive and loss-less propagation of the waves along the artery and models the reflection process as a linear system with a parameterized impulse response. Model parameters are determined from the measured Doppler waveforms by constrained optimization. Velocity waveforms were obtained from 142 pregnant volunteers where 123 met data quality criteria in at least one umbilical artery. The reflection model was consistent with the measured waveforms in 183 of 212 arteries that were analyzed. The analysis method was validated by applying it to simulated datasets and comparing solutions to ground-truth. With measurement noise levels typical of clinical ultrasound, parameters describing the reflected wave were accurately determined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".