Accuracy of Prenatal Ultrasound in Detecting Growth Abnormalities in Triplets: A Retrospective Cohort Study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".