The 30-Year Outcomes of Tetralogy of Fallot According to Native Anatomy and Genetic Conditions
Bibliographic record
Abstract
BACKGROUND: The reported survival of tetralogy of Fallot (TOF) is > 97%. Patients with pulmonary atresia and/or genetic conditions have worse outcomes, but long-term estimates of survival and morbidity for these TOF subgroups are scarce. The objective of this study was to describe the 30-year outcomes of TOF according to native anatomy and the coexistence of genetic conditions. METHODS: The TRIVIA (Tetralogy of Fallot Research for Improvement of Valve Replacement Intervention: A Bridge Across the Knowledge Gap) study is a retrospective population-based cohort including all TOF subjects born from 1980 to 2015 in Québec. We evaluated all-cause mortality by means of Cox proportional hazards regression, and cumulative mean number of cardiovascular interventions and unplanned hospitalisations with the use of marginal means/rates models. We computed 30-year estimates of outcomes according to TOF types, ie, classic TOF (cTOF) and TOF with pulmonary atresia (TOF-PA), and the presence of genetic conditions. RESULTS: We included 960 subjects. The median follow-up was 17 years (interquartile range, 8-27). Nonsyndromic cTOF subjects had a 30-year survival of 95% and had undergone a mean of 2.8 interventions and 0.5 hospitalisations per subject. In comparison, TOF-PA subjects had a lower 30-year survival of 78% and underwent a mean of 8.1 interventions, with 4 times as many hospitalisations. The presence of a genetic condition was associated with lower survival (< 85% for cTOF and < 60% for TOF-PA) but similar numbers of interventions and hospitalisations. CONCLUSIONS: The anatomic types and the presence of genetic conditions strongly influence the long-term outcomes of TOF. We provided robust 30-year estimates for key markers of prognosis that may be used to improve risk stratification and provide more informed counselling to families.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".