Extra‐cardiac diagnoses and postnatal outcomes of fetal tetralogy of fallot
Bibliographic record
Abstract
OBJECTIVE: Past studies of fetal tetralogy of fallot (ToF) have reported extra-cardiac anomalies (ECAs) in 17%-45%, genetic syndromes in as low as 20% and poor postnatal outcomes. This study sought to examine these factors in a contemporary cohort. METHODS: A retrospective review examining 83 fetuses with ToF diagnosed 2012-2019. Referral indication, ToF subtype, additional cardiac, extra-cardiac and genetic diagnoses, pregnancy outcomes and survival were documented. RESULTS: The mean gestational age at diagnosis was 23 ± 4 weeks. Of 94% (78/83) with genetic testing (GT), 30% (23/78, 95%CI 21%-40%) had genetic anomalies (GA), including Trisomy 21 (39%, 9/23), 22q11 deletion (35%, 8/23), Trisomy 13 or 18 (17%, 4/23) and 9% (2/23) others. A further 4% (3/78) had VACTERL association. Forty-one percent (34/83, 95%CI 31%-52%) had ≥1 major ECA of whom 41% (14/34) also had a genetic anomaly. OUTCOMES: 22% (18/83) pregnancy termination, 5% (4/83) intrauterine death and 72% (60/83) live birth. Of live births, 3% (2/60) experienced neonatal death, 7% late death (4/60) and 90% (54/60) were alive at last follow-up (mean age 3.5 ± 2.4 years). CONCLUSION: In a cohort of fetuses with ToF and high rates of GT, compared to previous reports, GA were more common and there were similar rates of ECAs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".