Prediction of Small for Gestational Age: Accuracy of Different Sonographic Fetal Weight Estimation Formulas
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of various sonographic estimated fetal weight (sEFW) formulas for the prediction of small for gestational age (SGA) neonates. METHODS: A retrospective analysis of 6,126 fetal biometrical measurements performed within 3 days of delivery. SGA prediction was evaluated for various sEFW formulas by calculating the sensitivity, specificity, positive/negative predictive value (PPV/NPV), likelihood ratio (+LR/-LR), overall accuracy and area under the receiver operating characteristic curve (AUC). Systematic error, random error, proportion of estimates >10% of birth weights, actual and absolute weight differences were compared between SGA and non-SGA neonates. RESULTS: Overall, 638 (10.4%) neonates were SGA. There was considerable variation among formulas in sensitivity (mean ± SD, 62 ± 14.4%; range, 32.4-91.2), PPV (72.5 ± 10.7%; 45.8-95.6) and +LR (24.2 ± 10.9; 7.2-57.3), mild variation in specificity (96.6 ± 2.7%; 87.4-99.4), NPV (94.6 ± 5.3%; 72.2-98.9) and -LR (0.4 ± 0.1; 0.1-0.7) and minimal variation in AUC (mean, 0.93; range, 0.91-0.93). The majority of formulas had a lower accuracy for the SGA neonates, with systematic error and random error ranging from -4.2 to 14.3% and from 8.4 to 12.9% for SGA, and from -8.7 to 16.1% and from 7.2 to 10.5% for non-SGA, respectively. CONCLUSION: sEFW formulas differ in their accuracy for SGA prediction. In our population, the most accurate formula for SGA prediction was Hadlock's formula utilizing femur length, abdominal and head circumference.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".