Incremental Detection of Severe Congenital Heart Disease by Fetal Echocardiography Following a Normal Second Trimester Ultrasound Scan in Québec, Canada
Bibliographic record
Abstract
Background: The benefit of fetal echocardiograms (FE) to detect severe congenital heart diseases (SCHD) in the setting of a normal second-trimester ultrasound is unclear. We aimed to assess whether the increase in SCHD detection rates when FE are performed for risk factors in the setting of a normal ultrasound was clinically significant to justify the resources needed. Methods: This is a multicenter, population-based, retrospective cohort study, including all singleton pregnancies and offspring in Quebec (Canada) between 2007 and 2015. Administrative health care data were linked with FE clinical data to gather information on prenatal diagnosis of CHD, indications for FE, outcomes of pregnancy and offspring, postnatal diagnosis of CHD, cardiac interventions, and causes of death. The difference between the sensitivity to detect SCHD with and without FE for risk factors was calculated using generalized estimating equations with a noninferiority margin of 5 percentage points. Results: A total of 688 247 singleton pregnancies were included, of which 30 263 had at least one FE. There were 1564 SCHD, including 1071 that were detected prenatally (68.5%). There were 12 210 FE performed for risk factors in the setting of a normal second-trimester ultrasound, which led to the detection of 49 additional cases of SCHD over 8 years. FE referrals for risk factors increased sensitivity by 3.1 percentage points (95% CI, 2.3–4.0; P <0.0001 for noninferiority). Conclusions: In the setting of a normal second-trimester ultrasound, adding a FE for risk factors offered low incremental value to the detection rate of SCHD in singleton pregnancies. The current ratio of clinical gains versus the FE resources needed to screen for SCHD in singleton pregnancies with isolated risk factors does not seem favorable. Further studies should evaluate whether these resources could be better allocated to increase SCHD sensitivity at the ultrasound level, and to help decrease heterogeneity between regions, institutions and operators.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".