OP02.04: Is it time to stop routine third trimester growth scans after COVID‐19 infection in pregnancy?
Bibliographic record
Abstract
The aim of this study was to assess the rate of sonographic small for gestational age (SGA) in patients who underwent the recommended routine third trimester fetal growth assessment after COVID-19 infection in pregnancy at our outpatient maternal-fetal medicine centres in Calgary, Canada. This was a retrospective observational study of singleton pregnancies with confirmed COVID-19 infection in pregnancy. Those who underwent a growth ultrasound between 34 and 37 weeks from 1 March 2020, to 31 December 2022 were included. Patients with fetal, maternal, or placental risk factors for SGA were excluded. Estimated fetal weight (EFW) was calculated using Hadlock's three-parameter formula. SGA was defined as an EFW less than the 10th percentile on the Alberta livebirth weight growth chart used at our institution. The Hadlock fetal growth chart was used to corroborate, given its more widespread acceptance. A total of 1562 patients met inclusion criteria. In this population, 31 pregnancies were sonographically SGA based on the Alberta growth curves, which conferred a rate of 2.0%. These values were consistent with the Hadlock growth curves, which similarly found a total of 30 pregnancies to be SGA, which was a rate of 1.9%. Published data in the literature suggests that the incidence of SGA after COVID-19 infection in pregnancy is similar to baseline rates or less. SGA rates in our population were much lower than the expected baseline rate. This adds to the existing literature which suggests that COVID-19 infection in pregnancy is not associated with SGA and will help to guide routine practice at our institution. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 0.001 |
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".