Accuracy of Prenatal Ultrasound in Detecting Growth Abnormalities in Triplets: A Retrospective Cohort Study
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Bibliographic record
Abstract
Significant management decisions in triplet pregnancies are made based mainly on ultrasound measurements of fetal growth, although there is a paucity of data examining the accuracy of fetal weight measurements in these gestations. To evaluate accuracy of prenatal ultrasound to diagnose growth abnormalities (intrauterine growth restriction, severe growth discordance) in triplet pregnancies, a retrospective cohort study of 78 triplet pregnancies (234 fetuses) delivered at a single tertiary hospital from January 2004 to May 2015 was performed. Growth percentiles from the last ultrasound were derived from estimated fetal weight using Hadlock's formula for each triplet. Growth discordance was calculated for each triplet set using the formula {(estimated fetal weight largest triplet - estimated fetal weight smallest)/estimated fetal weight largest}. These estimations were compared to birth weights. Sensitivity of ultrasound to predict ≥1 growth restricted fetus in a triplet set was 55.6% [95% CI 35.3, 74.5]; specificity was 100% [95% CI 93.0, 100]; positive predictive value (PPV) 100% [95% CI 74.7, 100]; negative predictive value (NPV) 81.0% [95% CI 73.2, 85.7%]. Sensitivity of ultrasound to detect fetal growth discordance >25% in a triplet set was 80.0% [95% CI 44.4, 97.5], specificity 94.1% [95% CI 85.6, 98.4]; PPV 66.7% [95% CI 42.4, 84.5]; NPV 97.0% [95% CI 90.2, 99.1]. Prenatal ultrasound currently remains the most reliable tool to screen for growth anomalies in triplet pregnancies; however, it appears to have less than ideal sensitivity, missing a number of cases of intra-uterine growth restriction and significant growth discordance.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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 it