Survey of multinational surgical management practices in tetralogy of Fallot
Bibliographic record
Abstract
BACKGROUND: A wide variety of surgical strategies are used in tetralogy of Fallot repair. We sought to describe the international contemporary practice patterns for surgical management of tetralogy of Fallot. METHODS: Surgeons from 18 international paediatric cardiac surgery centres (representing over 1800 tetralogy of Fallot cases/year) completed a Research Electronic Data Capture-based survey. Participating countries include: China (4), India (2), Nepal (1), Korea (1), Indonesia (1), Saudi Arabia (3), Japan (1), Turkey (1), Australia (1), United States of America (2), and Canada (1). Summary measures were reported as means and counts (percentages). Responses were weighted based on case volume/centre. RESULTS: Primary repair is the prevalent strategy (83%) with variation in age at elective repair (range). Approximately 47% of sites use patient age as a factor in determining the strategy, with age 90% of all trans-annular repairs. CONCLUSIONS: In this cohort representing 11 countries, there is variation in tetralogy of Fallot surgical management with no consensus on standard of practice. A large international prospective cohort study would allow analysis of impact of underlying anatomy and repair strategy on early and late outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".