The Role of Cardiopulmonary Testing to Risk Stratify Tetralogy of Fallot Patients
Bibliographic record
Abstract
Neonatal repair has completely changed the clinical history of patients with tetralogy of Fallot (ToF); however, these patients carry a significant risk of severe arrhythmias and sudden cardiac death in the long term. The exact mechanism for late sudden cardiac death is multifactorial and still not well defined, and the risk stratification for primary prophylaxis in these patients remains challenging. Cardiopulmonary exercise testing (CPET) is a well-established and safe method to assess cardiopulmonary function in children and adults with congenital heart disease. Several parameters obtained with CPET have been identified as potential prognostic of major adverse cardiovascular events in congenital heart disease. CPET is routinely used to assess functional capacity also in patients with ToF, and there is some evidence showing its usefulness in predicting the cardiac adverse events in patients with repaired ToF. Current guidelines recognize the importance of CPET in the evaluation and management of patients with ToF, but there is no clear consensus on which the CPET parameter or level of exercise intolerance, as measured by CPET, is truly predictive of an increased risk of arrhythmia and major adverse cardiovascular events in this population. Therefore, the aim of this narrative review is to describe the current evidence on the potential use of CPET in the risk stratification of patients with repaired ToF.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".