Application of Parametric Hazard Analysis with Time-dependent Covariables to Analyze Durability of Right Ventricle-to-pulmonary Artery Conduits Implanted in Infants and Young Children with Congenital Heart Disease
Bibliographic record
Abstract
Background: Interpretation of survival analyses is difficult if time-dependent procedures affecting the outcome are not fully accounted for. This thesis describes a unique modification of parametric hazard analysis incorporating therapeutic events as time-dependent covariables, and demonstrates this method's application to generate clinically relevant models used to inform management decisions.Methods: From a cohort of young recipients of vascular conduits, a parametric hazard model of conduit durability was created. Each child's longitudinal record was divided into segments representing unique states. Risk factors for earlier conduit replacement were generated by multivariable regression. The parametric equation was solved to create prediction plots.Results: The predictive model included time-dependent and time-independent covariables. Prediction plots illustrate conduit durability based upon different clinical scenarios.Conclusion: Addition of time-varying covariables into parametric hazard analysis controls for time-dependent risk factors unaccounted for by conventional methods, and may increase the accuracy of predictive model estimates.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".