The TRIVIA Cohort for Surgical Management of Tetralogy of Fallot: Merging Population and Clinical Data for Real-World Scientific Evidence
Bibliographic record
Abstract
BACKGROUND: Bridge Across the Knowledge Gap (TRIVIA) study is to evaluate the long-term impact of the surgical management strategies in TOF. The specific objectives are: (1) to describe the long-term outcomes of TOF according to the native anatomy and the presence of genetic conditions, (2) to evaluate the long-term outcomes of surgical repair according to associated residual lesions, and (3) to evaluate the impact of paediatric pulmonary valve replacements on the long-term outcomes. METHODS: The TRIVIA study is a population-based cohort including all subjects with TOF in the province of Québec between 1980 and 2017. It links patient-level granular clinical data with long-term administrative health care data. We will evaluate mortality, cardiovascular interventions, and hospitalizations for adverse cardiovascular events using survival Cox models and marginal mean/rates models for recurrent events, respectively. Multivariate multilevel models will correct for potential confounders, and risk score matching will mitigate the potential of confounding by indication. RESULTS: The current TRIVIA cohort includes 1001 eligible subjects with TOF with complete lifelong follow-up for > 98%. The median follow-up is 17.1 years, totalling > 17,000 patient-years. CONCLUSIONS: Universal health insurance data combined with granular clinical data enable the development of population-based cohorts, to which contemporary statistical methods are applied to address important research questions in congenital heart disease research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".