Gestational age and infection are greater predictors of placental histopathology than maternal prepregnancy BMI
Bibliographic record
Abstract
Abstract The placenta undergoes morphological and functional adaptions to adverse exposures during pregnancy. The effects of suboptimal maternal body mass index (BMI), preterm birth, and infection on placental histopathological phenotypes remain unclear, despite the association between these conditions and poor offspring outcomes. We hypothesized that suboptimal maternal prepregnancy BMI and preterm birth (with and without infection) would associate with altered placental maturity and morphometry, and that altered placental maturity would associate with poor birth outcomes. Clinical data and human placentae were collected from 96 pregnancies where mothers were underweight, normal weight, overweight, or obese, without other major complications. Placental histopathological characteristics were scored with an anatomical pathologist. Associations between maternal BMI, placental pathology (immaturity and hypermaturity), placental morphometry, and infant outcomes were investigated at term and preterm, with and without infection. Fetal vascular endothelium volumetric proportion was decreased, whereas syncytial knot volumetric proportion was increased, in placentae from preterm pregnancies with chorioamnionitis compared to term placentae. At term and preterm, pregnancies with overweight and obesity had a high percentage increase in proportion of immature placentae compared to normal weight. Placental maturity did not associate with infant birth outcomes. We observed placental hypermaturity and altered placental morphometry among preterm pregnancies with chorioamnionitis, suggestive of altered placental development, which may inform about pregnancies susceptible to preterm birth and infection. Our data increase our understanding of how common metabolic exposures and preterm birth, in the absence of other comorbidities or perinatal events, potentially contribute to poor pregnancy outcomes and the programming of offspring development.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".