OC02.02: Prediction of perinatal outcomes by third trimester fetal biometry and maternal characteristics
Bibliographic record
Abstract
To determine the role of third trimester fetal biometry with maternal characteristics to predict adverse perinatal outcomes. The IRNPQ/3D project was a multicentre prospective singleton cohort study in Canadian hospitals. We compared different screening methods based on third trimester fetal biometry: abdominal circumference z-score (AC) based on Hadlock curves (ACH) or Intergrowth 21st study curves (ACI), fetal weight estimation z-Score based on Hadlock formula (EFWH) and based on customised growth curves by Gardosi model (EFWG) as predictors of adverse perinatal outcome (APO). APO was defined by at least one of: stillbirth, neonatal death, Caesarean section for fetal distress, Apgar score less than 7 at 5 minutes and admission to neonatal intensive care unit. Multivariable regressions were done for each method with or without maternal characteristics in third trimester. A total of 2366 patients met the inclusion criteria and 264 (11%) with APO. APO was more frequent in patients who were nulliparous, used assisted reproduction, had a higher BMI, diabetes mellitus and a higher third trimester mean arterial pressure. Fetal biometry alone did not predict APO. When combined with maternal characteristics, prediction models improved with significant AUC (p < 0.0001): 0.643 for EFWG and 0.640 for CAH, CAI and EFWH. With the addition of maternal characteristics, the detection rate at 10% false positive rate increased from 13.3% to 20.6%, 13.3% to 20.6%, 12.4% to 22.8% and 11.0% to 20.6% for ACH, EFWH, EFWG and ACI, respectively. Fetal biometry is a poor predictor of APO and addition of maternal characteristics marginally improves the prediction. Sensitivities remain very low for routine screening and further studies are needed to test the addition of Doppler measurements and/or biological characteristics to predict perinatal outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".