Characterizing the Use of Exercise Testing in Repaired Tetralogy of Fallot Patients: A Multi-Institutional Survey
Bibliographic record
Abstract
Long-term survival for repaired Tetralogy of Fallot (rTOF) is excellent. We achieve this by close clinical monitoring to stratify prognosis and guide clinical decision-making. Cardiopulmonary exercise stress testing (CPET) is used to help guide clinical decision making; however, there are no clear guidelines for its use in this population. We sought to describe practice variability with regards to exercise testing for rTOF patients and how exercise data is used to guide management. We distributed a survey to pediatric cardiologists via email. Analyses were performed using qualitative statistics, two-sample T-tests, and chi-squared analysis. One-hundred and three clinicians completed the survey with 83% reporting that they routinely send symptomatic rTOF patients for exercise testing and 59% for asymptomatic patients. Respondents who routinely test asymptomatic patients reported higher levels of perceived helpfulness of exercise testing (p = 0.04) and comfort with CPET interpretation (p = 0.03). Nearly all respondents (92%) reported changing management primarily based on exercise testing results, with 62% reporting "occasionally changing management" and 10% reporting "frequently changing management". Results indicated that exercise test results influenced clinical decisions, such as the timing of interventions, need for additional imaging, or the initiation of exercise interventions. There was a statistically significant relationship between the perceived helpfulness of exercise testing and the likelihood of management changes (p < 0.01). The variability in attitudes and practices highlights the need for evidence-based guidelines addressing exercise testing in rTOF, particularly for asymptomatic patients.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".