P02.02: Can placental measurements in first trimester predict adverse pregnancy outcomes in twin pregnancies?
Bibliographic record
Abstract
To determine whether first trimester sonographic placental assessment, including novel 3D measurements, can predict adverse outcome in twin pregnancies. We conducted a prospective study of patients with twin pregnancies who underwent routine assessment at the time of nuchal translucency examination. We obtained 2D and 3D measurements of the placenta. We also evaluated placental volume (PV) via 3D techniques. Sonographic variables were analysed as predictors of a composite adverse pregnancy outcome. To date 41 pregnant women were recruited with 16 twin deliveries. Of these 4 (25%) had a COMP. The maximal placental thickness was greater in the COMP group both in 2D and 3D, placentas of pregnancies with COMP had shorter maternal and fetal surface in 2D but these differences did not reach statistical significance. Nevertheless, the distance between the two cord insertion sites was shorter in the group of COMP compared to normal twin pregnancies in the 2D examination; this difference approached statistical significance. The results of this pilot study suggest an association between first trimester placental measurements and adverse pregnancy outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".