OP20.04: Detecting placental pathology based on umbilical artery wave mechanics
Bibliographic record
Abstract
The association between intrauterine growth restriction (IUGR) and abnormally pulsatile Doppler waveforms in the umbilical artery (UA) has led to the widespread use of Doppler ultrasound as a screening tool in at-risk pregnancies. However, in cases of late onset IUGR, which accounts for 50% of unexplained stillbirths, the sensitivity and specificity of UA Doppler is poor. One explanation for this poor specificity is that pulsatility in the UA is both a function of heart motion and downstream placental vascular resistance. Here, we propose to overcome this limitation with a new ultrasound approach to measure pulsation in the UA in which the waveforms are decomposed into forward propagating waves (developed by the heart) and reverse propagating waves reflected back from the placental circulation. A proof-of-principle experiment was conducted in mice. Pregnant CD1 (n = 6) and C57BL/6 (n = 11) mice were examined by ultrasound at 17.5 days gestation. Velocity and diameter waveforms were measured in the UA with high frequency (40 MHz) pulsed-wave Doppler and M-mode ultrasound respectively. The data were aligned, decomposed into their forward incident and backward reflected components and summarised in terms of their relative amplitude, timing and dispersion. On average, the relative amplitude of the reflected wave was 38 ± 12% larger in C57BL/6 fetuses (p < 0.01). Time delay and dispersion of the reflected waves were similar between the two strains. This observation of differing reflected wave amplitudes between the two strains is consistent with our previous finding of C57BL/6 fetuses demonstrating blunted fetoplacental capillarisation and larger fetoplacental arteries, as compared to CD1 fetuses. Moreover, by accounting for cardiac function when screening for placental abnormalities, this wave reflection methodology has the potential to improve the detection of late onset IUGR due to placental disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".