Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning
Bibliographic record
Abstract
Multiple Cervical Length (CL) measurements are typically acquired throughout the course of twin pregnancy to detect the early stages of labour and identify pregnancies at a high risk of preterm delivery. This study uses Machine-Learning (ML) approaches to determine the optimal timing of repeated CL measurements when used for predicting spontaneous preterm birth (sPTB) in twin pregnancies. Serial CL measurements from ultrasounds performed between 16 and 28 weeks of gestation were retrospectively acquired from 2,095 patients carrying twin pregnancies. These measurements were used for creating several CL feature sets, which were subsequently evaluated for their utility in predicting PTB < 37, sPTB < 37, sPTB < 34, and sPTB < 32 weeks. The highest accuracies for predicting sPTB < 37, sPTB < 34, and sPTB < 32 were found for the Logistic Regression model, which performed at 58%, 63%, and 73%, respectively. Post-hoc analysis showed that using multiple CL measurements did not significantly improve the sPTB prediction accuracy, irrespective of the ML model. Specifically, a single CL measurement at 18-20 weeks of gestation was sufficient for predicting sPTB < 32 weeks with the same accuracy. Future work should expand patient cohorts by including early CL measurements and investigating the time between a CL exam and sPTB from a regression standpoint.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".