Use of Doppler velocimetry in diagnosis and prognosis of intrauterine growth restriction (IUGR): A Review
Bibliographic record
Abstract
Intrauterine growth restriction (IUGR) is a condition which has been difficult to assess at an early stage, resulting in the delivery of children who have poor genetic growth potential. Currently, IUGR classification is based upon the system of ultrasound biometry. Doppler velocimetry allows the measurement of hemodynamic flow of major fetal vessels, comparing the flow indices and patterns of normal and IUGR cases. In this review, the effectiveness of Doppler velocimetry in assessing blood flow in major vessels including the umbilical artery, ductus venosus, and middle cerebral artery was studied for both diagnostic and prognostic screening of IUGR. The umbilical artery is the most frequently studied vessel in Doppler velocimetry due to its accessibility and the strength of its associations with fetal outcomes. Abnormalities in the ductus venosus waveform can be indicative of increased resistance in the right atrium due to placental abnormalities. The middle cerebral artery is the most studied fetal cerebral artery and can detect cerebral blood flow and direction, which is why these three vessels were selected to be examined in this context. A potential mathematical model could be developed to incorporate these Doppler measurements which are indicative of IUGR, in order to reduce perinatal mortality. The purpose of the proposed algorithm is to integrate Doppler velocimetry with biophysical profiling in order to determine the optimal timing of delivery, thus reducing the risks of adverse perinatal outcomes.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".