Addressing Survivorship Bias in Neurocognitive Outcomes After Early Complex Cardiac Surgery Using Clustering and Propensity Scores
Bibliographic record
Abstract
Background: Although advances in cardiac surgery have increased survival rates from congenital heart disease, neurocognitive and functional outcomes have not significantly improved. We hypothesized that the absence of change in outcome scores may be due to survivorship bias. Our study aimed to address this by adjusting neurocognitive and functional outcome trend lines using k-mean clustering and propensity score (PS) methods. Methods: Prospective follow-up was conducted on 266 children with single ventricle congenital heart disease who underwent the Norwood procedure at age ≤6 weeks at Stollery Children's Hospital, Edmonton, Alberta, between 1997 and 2016. PS and k-mean clustering methods were used to adjust outcomes for children with more complex conditions. Crude and adjusted trend lines for neurocognitive and functional outcomes were analyzed using multiple linear regression models. Results: Multiple logistic regression determined age at surgery, total ventilation days, deep hypothermic circulatory arrest time, and total days chest open were significant in PS calculation. The adjusted linear time-trend analysis of neurocognitive and functional outcomes showed no change in Full Scale Intelligence Quotient and Visual Motor Integration scores. Although not robust to using the different PS adjustment methods, General Adaptive Composite scores may have decreased over time. Models with PS adjustment were not different from models without PS adjustment. Conclusions: PS adjustment with clustering did not confirm survivorship bias. PS-adjusted models demonstrated stable Full Scale Intelligence Quotient and Visual Motor Integration scores over time, and inconsistent findings for General Adaptive Composite scores. Improved survival after more complex cardiac surgery was not associated with worse long-term neurocognitive 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 | 0.026 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".