Utility of competing risks in analyzing outcomes of repair of congenital heart defects
Bibliographic record
Abstract
Background and objectives. Congenital heart disease outcomes analysis often involves competing endpoints. Generation of clinically relevant competing risks (CR) models providing therapeutic inferences is demonstrated. Conclusions. CR is useful in analyzing outcomes after congenital heart surgery. Clinically relevant multivariable models demonstrating the impact of important risk factors on outcomes can be generated. For ACHD, CR defined prevalence of hospital mortality (4.5%) and discharge (95.5%). Single CR model simultaneously analyzed mortality and LOS and illustrated risk factor impact on early outcomes. Results. In PAIVS, CR prevalence of 15-year end-states were: 2-ventricle repair, 33%; Fontan, 20%; pre-repair death, 38%; other, 9%. Morphologically-driven institutional protocols mitigate impact of unfavorable morphology. Methods. CR methodology was used to quantify cumulative incidence and risk factors for: (1) death and definitive repair states in 408 neonates with pulmonary atresia-intact ventricular septum (PAIVS); (2) hospital mortality and discharge in 1351 adult congenital cardiac operations (ACHD).
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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 | 0.118 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".