Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles
Bibliographic record
Abstract
OBJECTIVE: To assess whether machine learning (ML) offers improved birth weight prediction accuracy, since despite numerous models, the Hadlock formula remains the clinical standard. METHODS: A multicenter retrospective study analyzed data from 9674 singleton pregnancies with estimated fetal weight (EFW) within 7 days of delivery. ML models-Linear Regression, Decision Tree, Random Forest, LightGBM, XGBoost, and Neural Networks-were trained using ultrasound and maternal features. Performance was measured by mean absolute percentage error (MAPE), root mean squared error (RMSE), mean absolute error (MAE), accuracy, precision, recall, and F1-score for percentile categories. RESULTS: LightGBM and XGBoost outperformed Hadlock in overall weight estimation (MAPE ~0.065; RMSE ~252; MAE ~190). For birth weight percentiles (<3rd, <10th, >90th, >97th), ML showed marginal or comparable improvement. LightGBM had higher accuracy and F1 for extreme percentiles, whereas Hadlock showed slightly better recall in some cases. CONCLUSION: ML models, especially LightGBM and XGBoost, enhanced overall weight prediction but offered limited gains in identifying percentile-based risk. The Hadlock formula remains a strong tool for categorizing at-risk fetuses.
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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 | 0.015 | 0.029 |
| 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".