Influence of ultrasound‐to‐delivery interval and maternal–fetal characteristics on validity of estimated fetal weight
Bibliographic record
Abstract
OBJECTIVES: To explore the effects of ultrasound-to-delivery interval and maternal-fetal characteristics on the distribution of measurement error in estimated fetal weights (EFWs), and to determine the predictive ability of EFW for diagnosis of small-for-gestational age (SGA) and large-for-gestational age (LGA) among infants delivered within 1 day of an ultrasound examination. METHODS: Percentage differences between EFW and birth weights were calculated in 3697 pregnancies. Linear regression was used to compare the accuracy of EFW for births on each of the 6 days after an ultrasound scan with the accuracy observed among births on the same day. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value for diagnosis of SGA and LGA according to EFW was assessed. RESULTS: The mean +/- SD percentage difference among deliveries within 1 day of the last ultrasound scan was 0.2 +/- 9.0%. Mean percentage differences were not significantly different from day 0 on days 1, 2 and 3; however, combining the data from these 4 days obscured a slight bias towards an overestimation of weight evident on day 0 and day 1. Among deliveries within 1 day of an ultrasound scan, the PPV was 61% for SGA diagnosis and 54% for LGA diagnosis. CONCLUSION: Combining data from births > 1 day after the last ultrasound examination may lead to a false conclusion that there is systematic underestimation of weight. EFW tended to underestimate the weight of macrosomic fetuses and overestimate that of small fetuses which limited sensitivity and PPV. Maternal-fetal characteristics are weak predictors of individual errors in EFW.
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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 | 0.038 | 0.284 |
| Meta-epidemiology (narrow) | 0.000 | 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".