OC22.01: Prediction of small‐for‐gestational age neonates by third trimester fetal biometry: impact of ultrasound‐delivery interval
Bibliographic record
Abstract
To compare different third trimester ultrasound screening methods to predict smallness for gestational age (SGA) and to evaluate the impact of the interval between last measurement and delivery. Data were collected from the IRNPQ/3D project: a multicenter prospective singleton cohort study in Canadian hospitals. We compared different ultrasound screening methods, including Abdominal circumference z-score based on Hadlock curves (ACH); AC Z-score based on Intergrowth 21st study curves (ACI); fetal weight estimation Z-score from Hadlock formula and curves (EFWH); fetal weight estimation based on customised curves from Gardosi model EFWG); and the fetal growth velocity (FGVAC) based on CA growth between the second and third trimester. The methods were compared to predict SGA with a delay from delivery and last ultrasound <4 weeks, < 6 weeks, < 10 weeks, by calculating area under the ROC curve (AUC) and detection rates. Out of 2366 patients in the total cohort, 1832 had a third trimester ultrasound performed (median gestational age 32 weeks, IQR 32–33 weeks), including 190 with SGA (8%). The ultrasound-delivery interval was 4 weeks or less in 13% cases, 6 weeks or less in 37%, 10 weeks or less in 74% of cases. The best predictors with a delay < 4 weeks were ACH (AUC 0.856 CI95%[0.760–0.962]) and ACI (AUC 0.853 [0.753–0.954]); with a delay < 6 weeks: ACH (AUC 0.824 [0.758–0.882]) and EFWH (AUC 0.823 [0.763–0.883]); with a delay < 10 weeks: EFWH (AUC 0.784 [0.743–0.824]) and ACI (AUC 0.775 [0.733–0.817]). At a fixed 10% false-positive rate, detection rates were 60% for ACH and 60% for ACI with a delay < 4 weeks; 50.8% for ACH and 54.1% for EFWH with a delay < 6 weeks; 38.6% For EFWH and 40.0% for ACI with a delay < 10 weeks. Third trimester ultrasound measurements provide poor to moderate prediction for SGA. A shorter ultrasound-delivery interval provides a better performance. Further studies are needed to test the addition of maternal or biological characteristics for SGA screening.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".