Improving customized fetal biometry by longitudinal modelling
Bibliographic record
Abstract
OBJECTIVE: To develop customized biometric charts to better define abnormal fetal growth. METHODS: A total of 1056 singleton fetuses from the Raine Study underwent serial ultrasound biometry (abdominal circumference [AC], head circumference, and femur length) at 18, 24, 28, 34, and 38 weeks' gestation. Customized biometry trajectories were developed adjusting for epidemiological influences upon fetal biometry using covariates available at 18 weeks gestation. Prediction accuracy (areas under the receiver operating characteristic curve [AUC] and 95% confidence interval [95%CI]) was evaluated by repeated random sub-sampling cross-validation methodology. RESULTS: The model for derived estimated fetal weight (EFW) performed well for EFW less than 10th predicted percentile (AUC = 0.695, 95%CI, 0.692-0.699) and EFW greater than 90th predicted percentile (AUC = 0.705, 95%CI, 0.702-0.708). Fetal AC was also well predicted for growth restriction (AUC = 0.789, 95%CI, 0.784-0.794) and macrosomia (AUC = 0.796, 95%CI, 0.793-0.799). Population-derived, sex-specific charts misclassified 7.9% of small fetuses and 10.7% of large fetuses as normal. Conversely, 9.2% of those classified as abnormally grown by population-derived charts were considered normal by customized charts, potentially leading to complications of unnecessary intervention. CONCLUSIONS: Customized fetal biometric charts may offer improved ability for clinicians to detect deviations from optimal fetal growth and influence pregnancy management.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".