Early pregnancy immune profile and preterm birth classified according to uteroplacental lesions
Bibliographic record
Abstract
INTRODUCTION: Preterm birth is a heterogeneous phenotype, with placental abnormalities underlying many cases. The etiology of preterm births that occur in the absence of placental abnormalities, however, remain enigmatic and we considered that early pregnancy biomarkers may provide clues. METHODS: Women from a hospital-based cohort (2008-2012, n = 397) were randomly selected within 6 strata of term and preterm birth with and without placental decidual vasculopathy (arteriopathy), intrauterine inflammation/infection (acute chorioamnionitis), or no lesions. Lipids and inflammatory markers were analyzed in first trimester samples (12.5 ± 0.6 weeks) and related to outcome groups (referent, term births with no lesions). Factor analysis then clustered analytes and related these to preterm birth groups, adjusted for covariates and stratified by pre-pregnancy obesity. RESULTS: Three biomarker patterns were identified. Immune activation cytokines (33% of the variance) were associated with preterm birth with no lesions (aOR 1.5, 95%CI 1.1-2.1), particularly among obese women. In contrast, inflammatory chemokines (9% of variance) were associated with term and preterm vasculopathy among non-obese women (aOR 2.6 [1.3, 4.7] and 2.0 [1.1, 3.0], respectively). DISCUSSION: The early pregnancy maternal immune profile is related to preterm births classified according to placental lesions, and these associations vary according to obesity status.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".