Abstract 18375: Improving Rate of Prenatal Diagnosis of Critical Congenital Heart Disease Despite the Covid19 Pandemic: A Canadian Multiprovincial Study
Bibliographic record
Abstract
Background Restriction to travel, reallocation of health resources and physical distancing during the COVID19 pandemic caused extraordinary health system strain, requiring limited tertiary referral acceptance. The overall impact of public health measures during COVID19 on diagnosis of congenital heart disease (CHD) has not been explored. We sought to determine the rate, timing of diagnosis and pregnancy outcomes of critical CHD prior to and during the COVID era. Methods: Cases of CHD with due date/birth date from 1 Jan 2016 to 1 Mar 2022 that required or were anticipated to require neonatal intervention were identified from surgical and referring centres in Ontario and Alberta. Pregnancies were categorized as reaching 18 weeks GA before (pre-COVID) or after 1 March 2020 (COVID). Outcomes included timing of diagnosis (pre/postnatal), GA at prenatal diagnosis, and pregnancy outcome. Data are presented as mean, 95% CI or median (IQR); rank-sum comparison of continuous variables or Chi 2 comparison of proportions were used. Results: Prenatal diagnosis occurred in 1240/1823 (68% (65.8, 70.1)) of cases of critical CHD overall, with a pre-COVID rate of 867/1305 - 66% (64, 69) and COVID rate of 373/518 - 72% (68, 76), p=0.02. During COVID, earlier median GA at obstetric ultrasound (median GA: pre-COVID 20.1 (19.1, 22), COVID 19.9 (19.1,21.3), p=0.025) and diagnosis of CHD (median GA: pre-COVID 21.7 (20.3, 24.3), COVID 21.3 (20,23.3), p=0.006) occurred. Prenatal diagnosis before 22 weeks GA (pre-COVID 449/851, 52.3% vs COVID 219/371, 59%; p=0.043) and termination were more common during COVID (pre-COVID 28% (25,31), COVID 35% (30,40) p=0.015). Conclusion The rate of prenatal diagnosis of critical CHD continued to improve during the COVID pandemic in two of Canada’s largest provinces. Unexpectedly, obstetric ultrasounds occurred earlier, leading to earlier prenatal diagnosis of CHD. These findings may have implications for referral practices in the post-COVID era.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".