Advancing Prenatal Detection of Congenital Heart Disease: A Novel Screening Protocol Improves Early Diagnosis of Complex Congenital Heart Disease
Bibliographic record
Abstract
OBJECTIVES: Prenatal diagnosis of complex congenital heart disease (CHD) during routine obstetric ultrasound (US) examinations improves postnatal outcomes, but sensitivity is low (<40%). Our objective was to improve our prenatal detection of complex CHD with implementation of a specific screening protocol. METHODS: From January 2003 to December 2013, 506 consecutive confirmed cases of complex CHD in the province of Manitoba, Canada, were analyzed to compare the sensitivity and positive predictive value of prenatal US detection of complex CHD before and after the introduction of a novel prenatal screening protocol. The intervention was done in October 2004, emphasizing screening and not diagnosis of complex CHD. It involved education, practical scanning tips, a checklist, and feedback on cases. We also assessed the effect of the intervention in different screening settings: community hospitals, tertiary hospitals, and fetal assessment units. RESULTS: The sensitivity for detecting complex CHD increased from 29.8% to 88.3% (P < .0001), while the positive predictive value remained high. The largest improvement in detection was found for US units in community hospitals (52.4% higher; P < .0001), followed by tertiary hospitals (39.9%; P = .0004), and fetal assessment units (7.2%; P = .16). Additionally, there was a significant decrease in the presentation of neonates in critical condition from before to after the implementation (24.3% to 13.1%, respectively; P = .0165). CONCLUSIONS: Implementing a focused routine prenatal screening protocol can vastly improve detection rates of critical cardiac abnormalities. The improvement in community hospitals was particularly important because early detection ensured that the birth was planned to take place in an appropriate facility. Our novel protocol can be performed by all sonographers, regardless of experience, equipment used, and hospital setting.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".