Intraplacental Villous Artery Doppler as an Independent Predictor for Placenta-Mediated Disease and Its Comparison with Uterine Artery Doppler and/or Placental Biochemical Markers in Predictive Models: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To validate intraplacental villous artery (IPVA) Doppler as a predictor for placenta-mediated diseases (PMDs), to compare its predictive value with uterine artery (UtA) Doppler and placental biochemical markers, and to assess its value in predictive PMD models. METHODS: IPVA and UtA indices (pulsatility index [PI] and resistance index [RI]) were recorded at 18-24 weeks of gestation in a cohort of 117 women. The predictive values of IPVA, UtA, and placental biochemical markers were analyzed and compared between the PMD group (the women who developed preeclampsia or intrauterine growth restriction) and the non-PMD group (the women who remained healthy throughout pregnancy and 3 months postpartum) using the receiver-operating characteristic curves. Logistic regression was used to compare predictive models for PMDs based on IPVA, UtA, and/or biochemical markers. RESULTS: 31 (26.5%) women developed PMD (17 preeclampsia and 14 intrauterine growth restriction). IPVA PI was significantly higher in the PMD group than in the non-PMD group (p = 0.001). UtA PI and RI values remained nonsignificant between both groups (p = 0.066 and 0.104, respectively). IPVA PI from the 3 main branches of the placenta, and specifically the central main stem villi, showed a strong association with PMDs in comparison to UtA (p = 0.03 and 0.001 vs. 0.29). Model prediction including IPVA and UtA PI with or without placental biomarkers did not add any further significance to IPVA PI alone (p = 0.03, 0.41, and 0.36). CONCLUSIONS: IPVA PI appears superior to UtA PI or RI and placental biomarkers in PMD prediction. Model prediction for PMDs including IPVA, UtA Doppler, and biochemical markers did not enhance prediction values compared to IPVA Doppler alone.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".