Machine learning‐based placental clusters and their associations with adverse pregnancy outcomes
Bibliographic record
Abstract
BACKGROUND: Placental abnormalities have been described in clinical convenience samples, with predominately adverse outcomes. Few studies have described placental patterns in unselected samples. OBJECTIVE: We aimed to investigate associations between co-occurring placental features and adverse pregnancy outcomes in a prospective cohort of singletons. METHODS: Data were from the Safe Passage study (U.S. and South Africa, 2007-2015). Before 24 weeks' gestation, participants were randomly invited to donate placental tissue at delivery for blinded, standardised pathological examination. We used hierarchical clustering to construct statistically derived groups using 60 placental features. We estimated associations between the placental clusters and select adverse pregnancy outcomes, expressed as unadjusted and adjusted risk ratios (RRs) and robust 95% confidence intervals (CI). RESULTS: We selected a 7-cluster model. After collapsing 2 clusters to form the reference group, we labelled the resulting 6 analytic clusters according to the overarching category of their most predominant feature(s): severe maternal vascular malperfusion (n = 117), fetal vascular malperfusion (n = 222), other vascular malperfusion (n = 516), inflammation 1 (n = 269), inflammation 2 (n = 175), and normal (n = 706). Risks for all outcomes were elevated in the severe maternal vascular malperfusion cluster. For instance, in unadjusted analyses, this cluster had 12 times the risk of stillbirth (RR 12.07, 95% CI 4.20, 34.68) and an almost doubling in the risk of preterm delivery (RR 1.93, 95% CI 1.27, 2.93) compared with the normal cluster. Small infant size was more common among the abnormal clusters, with the highest unadjusted RRs observed in the fetal vascular malperfusion cluster (small for gestational age birth RR 2.99, 95% CI 2.24, 3.98, head circumference <10th percentile RR 2.86, 95% CI 1.60, 5.12). Upon adjustment for known risk factors, most RRs attenuated but remained >1. CONCLUSION: Our study adds to the growing body of epidemiologic research, finding adverse pregnancy outcomes may occur through etiologic mechanisms involving co-occurring placental abnormalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".