Impact of antenatal screening on the presentation of infants with congenital heart disease to a cardiology unit
Bibliographic record
Abstract
AIM: Antenatal diagnosis of congenital heart disease (CHD) facilitates prenatal treatment and optimal perinatal care. This has been demonstrated to improve perinatal mortality and morbidity in neonates with CHD. Thus, antenatal diagnosis of CHD is most likely to benefit patients who require surgery in early infancy. We aimed to examine the frequency of antenatal diagnosis in neonates presenting to The Royal Children's Hospital severe CHD. METHODS: Main outcome measures were antenatal diagnosis and whether the individual lesion would have been expected to be detected on a four-chamber view or four-chamber and outflow tract view during a routine obstetric anomaly ultrasound. Poisson regression was used to estimate the average trend over the study period. RESULTS: A total of 610 patients met the inclusion criteria, of whom 164 had an antenatal diagnosis (26.8%). If routine ultrasound screening was ideal, we would have expected 63.9% of cases to be detected on four-chamber view and 83.6% on four-chamber and outflow tract view. Trend analysis demonstrated an annual rate of improvement of 9% in actual versus expected antenatal diagnosis of CHD. Malformation-specific analysis showed that antenatal detection was the highest for double inlet/outlet ventricle (51.3%, 95% confidence interval 34.8-67.6%) and the lowest for simple transposition of the great arteries (15.6%, 95% confidence interval 9.0-24.5). CONCLUSION: Despite mass screening for congenital malformations in Victoria with routine antenatal ultrasounds, a large proportion of neonates with severe congenital heart disease still present without an antenatal diagnosis.
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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 | 0.029 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".