Sonographic fetal weight estimation percentiles should be interpreted with caution in the second trimester
Bibliographic record
Abstract
Abstract Objective To describe the limitations of sonographic estimation of fetal weight (EFW) percentiles during the second trimester; compare the accuracy of EFW percentiles to that of single fetal biometric indice; and identify the gestational age threshold beyond which the sonographic diagnosis of fetal growth restriction (FGR) becomes more reliable. Methods We described the limitations of EFW by simulation using the 1991 Hadlock. Then, we illustrated these limitations using data from a cohort of singleton pregnancies at a single tertiary center (2014–2023). The primary predictor was EFW percentile, and the primary outcome was birth weight <10th percentile for gestational age. The diagnostic accuracy was described using the area under the receiver‐operator characteristic curve, detection rate, false positive rate, and positive and negative predictive values. Results Less than 25 weeks of gestation, EFW percentiles are highly sensitive to minor dating or sonographic measurement errors. Abdominal circumference (AC) percentile is less sensitive to these errors and may be a more reliable measure. Using a cohort of 29 608 patients (76 378 ultrasound examinations), we confirmed that the positive predictive value (PPV) of EFW percentile for birth weight <10th percentile decline sharply before 25 weeks of gestation. In addition, before 25 weeks of gestation, AC <10th percentile has a higher PPV for birth weight <10th percentile than EFW <10th percentile (45% vs. 41% at 15 0/7 –19 6/7 weeks, and 61% vs. 56% at 20 0/7 –24 6/7 weeks). Conclusion Sonographic EFW percentiles are less reliable before 25 weeks than later in pregnancy, and should be interpreted with caution during this gestational age period.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".