Biopsychosocial Predictors of Preterm and Spontaneous Preterm Birth: A Machine Learning Analysis of the All Our Families Cohort
Bibliographic record
Abstract
Objective The primary objective of this study was to develop and evaluate a machine learning (ML) model for predicting preterm birth (PTB) and spontaneous preterm birth (SPTB) using biopsychosocial data. Design Secondary analysis of a cohort data. Sample Data from a prospective longitudinal pregnancy cohort, All Our Families, were used in the current study. Pregnant individuals prior to 25 weeks gestation with a medically low-risk pregnancy were eligible for recruitment. Methods ML classification models were trained to predict both SPTB and PTB using a total of 52 input features. Main Outcome Measures Machine learning model accuracy and the features selected. Results Moderate accuracies were achieved by the PTB (ROC-AUC = 0.62±0.03) and SPTB models (ROC-AUC = 0.57±0.05). For PTB, the most informative variables were a diagnosis of hypertensive disorder of pregnancy (HDP), feelings towards pregnancy, use of fertility treatment, satisfaction with social support, and exercise. For SPTB, the top predictive factors were use of fertility treatment, feelings towards pregnancy, diagnosis of HDP, household income, and satisfaction with social support. Conclusions The current study sets the stage for further research to use ML models to predict perinatal outcomes and examine novel and potentially modifiable biopsychosocial factors contributing to the overall risk of PTB.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".