Accuracy of sonographic fetal biometry in estimating intertwin size discordance at birth
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of estimated fetal weight (EFW) and abdominal circumference (AC) discordance in diagnosing large birthweight (BW) discordance and selective fetal growth restriction (sFGR). METHODS: Retrospective cohort study of patients with twin pregnancies followed at a tertiary center (N = 1065). We determined the accuracy of intertwin fetal size discordance (based on either EFW or fetal AC) at the last ultrasound exam before birth in estimating birthweight discordance and in diagnosing sFGR at birth. RESULTS: EFW discordance was more accurate than AC discordance in estimating BW discordance as reflected by a smaller systematic error (-0.97% vs. -6.43%, respectively, P < 0.001) and mean absolute percentage error (5.70% vs. 7.35%, respectively, P < 0.001), and a larger proportion of cases with discordance within 5%, 10%, or 15% of BW discordance (53.1% vs. 45.4%, 84.3% vs. 72.4%, and 95.1% vs. 87.0%, respectively). Still, both EFW discordance and AC discordance had low diagnostic accuracy for large BW discordance and sFGR. For example, EFW discordance >20% had a sensitivity of 49.1% and a positive predictive value of 57.0% for BW discordance >20%, and the antenatal diagnosis of sFGR had a sensitivity of 45.8%-54.4% and a positive predictive value of 50.9%-55.2% for the postnatal diagnosis of sFGR. CONCLUSION: While EFW discordance was more accurate than AC discordance in estimating BW discordance, both measures had low diagnostic accuracy for large BW discordance and sFGR. Care providers should consider the limited diagnostic accuracy when making management decisions on the timing and mode of delivery.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".