VP31.12: Trends in fetal ultrasound referrals and case severity throughout the first and second waves of the COVID‐19 pandemic
Bibliographic record
Abstract
To determine the combined and differential impact of individual waves of the COVID-19 pandemic on fetal ultrasound referral patterns and antenatal case severity. This was a cross-sectional study evaluating temporal trends in fetal ultrasound referral patterns and case severity at a regional fetal assessment unit. Fetal assessment scans performed during the first and second ‘waves’ of the pandemic (March to May 2020 and November to March 2021) were compared head-to-head and to baseline pre-COVID-19 patterns. Clinic lists and stored ultrasound reports were reviewed to collate maternal demographics and medical history, referral details, daily scan volumes, ultrasound findings, and level of case severity, in order to compare outcomes across the pandemic. 9,451 fetal ultrasounds were performed during the first and second waves of the pandemic, and represent a significant reduction from baseline volumes in the unit (p = 0.008). In the first wave, there was a significant decrease in referrals for early pregnancy complications, genetic ultrasounds, and diabetes surveillance (p < 0.01): there were also fewer referrals from rural communities and general practitioners. The second-wave of the pandemic represented a further decline in referrals from remote locations, but less disruption in number of scans for maternal medical complications. The level of high-acuity cases increased significantly throughout the pandemic compared to baseline (4.9% vs 3.3%; p = 0.0031), but was higher in the first as compared to the second-waves. Also, there was an almost 10% increase in the relative proportion of stillbirths diagnosed during the pandemic, although the absolute numbers remain small. Fetal ultrasound volumes decreased and case severity increased throughout the pandemic, as well as an increase in stillbirths. Enhanced understanding of the impact of access to prenatal care and utilisation of services on the direct and indirect risks for pregnant women is critical for improving care and outcomes during future pandemic planning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".