Optimal Management of the Cyanotic Neonate With Tetralogy of Fallot—A Clinical Decision Analysis
Bibliographic record
Abstract
Background: Management options for critically cyanotic neonates with tetralogy of Fallot include primary repair, ductal or right ventricular outflow tract stents, and surgical shunts. However, rigorous comparisons between these strategies are precluded by small numbers, lack of equipoise, and center-specific bias. Methods: A Markov model decision tree with Monte Carlo microsimulations was constructed to model 2-year outcomes for a hypothetical cohort of 10,000 cyanotic tetralogy of Fallot neonates eligible for all 3 strategies. Input transition state probabilities, utilities, and costs were derived from representative published reports. Outcomes were used to determine quality-adjusted life-years and costs after 50 model iterations. The incremental cost-effectiveness ratio was calculated to determine the preferred strategy. Sensitivity and threshold analysis varied probabilities of 40 factors to identify values at which the preferred strategy would switch. Results: From modeling, immediate mortality from index procedure favored staged approaches, but total mortality favored primary repair after approximately 6 months. Cumulative 2-year mortality from modeling was 8.1%, 11.6%, and 12.4% for primary repair, stenting, and shunting, respectively. Calculated incremental cost-effectiveness ratios identified primary repair as the preferred strategy, followed by stenting and then shunting. Sensitivity and threshold analysis identified total pathway cost to be the only determinant of altered strategy preference with respect to primary repair. For comparisons of staged approaches, several variables reflecting cost and outcomes were identified. Conclusions: Our modeling suggests that primary repair may be superior to staging with stent or shunt for cyanotic neonates with tetralogy of Fallot, with improved 2-year morbidity, mortality, and cost utility.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".