Choice of Formula and Accuracy of Fetal Weight Estimation in Small‐for‐Gestational‐Age Fetuses
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to identify the most accurate sonographic models for fetal weight estimation in specific subgroups of small-for-gestational-age (SGA) fetuses. METHODS: We conducted a retrospective study of women who delivered an SGA neonate and underwent a sonographic estimation of fetal weight within 7 days of delivery in a single tertiary center (n = 370). The accuracy of fetal weight estimation was compared for 33 sonographic models (27 nontargeted and 6 targeted SGA- or low-birth-weight-specific models) in specific subgroups of SGA fetuses: early versus late SGA, asymmetric versus symmetric, and presence of Doppler abnormalities. RESULTS: A wide variation in the accuracy of the different models was found (systematic error, -12.5% to 15.1%; random error, 7.8% to 15.5%). Most nontargeted models tended to systematically overestimate the weight of SGA fetuses. The best performing model in the overall SGA group was the targeted model of Scott et al (J Ultrasound Med 1996; 15:669-672; systematic error ± random error, -2.8% ± 8.3%). However, the optimal models varied for different subgroups of SGA fetuses, and in most cases the targeted models were the most accurate. An approach that used the optimal model for each subgroup of SGA fetuses compared with the uniform use of the model of Scott et al for all SGA fetuses was associated with a lower systematic error (-0.38% versus -2.8%; P < .001) and a higher proportion of weight estimations within 5%, 10%, and 15% of birth weight (48.4% versus 40.8%; P= .038; 78.6% versus 71.4%; P= .022; 95.1% versus 89.2%; P = .003, respectively). CONCLUSIONS: Sonographic models in current use for fetal weight estimation in SGA fetuses have significant errors, and their performance varies for specific subgroups of SGA fetuses. An approach that uses subgroup-specific models may improve the accuracy of weight estimation among SGA fetuses.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".