Abstract 8160: Risk Factors For Hospital Mortality And Morbidity Following The Norwood Procedure: Results From The Multicenter Single Ventricle Reconstruction Trial
Bibliographic record
Abstract
BACKGROUND We sought to identify risk factors for mortality and morbidity during the Norwood hospitalization in a large prospective cohort of newborns with hypoplastic left heart syndrome and variants enrolled in the Single Ventricle Reconstruction trial. METHODS Potential predictors for outcome included patient and procedure related variables and center/surgeon volumes. Outcome variables occurring during the Norwood procedure and prior to hospital discharge or stage 2 surgery included: mortality, end-organ complications, length of ventilation and hospital length of stay (LOS). Variables with univariate p ≤ .2 were used as candidate predictors for multivariable regression modeling (p < .05, significant). A reliability estimate of greater than 50% obtained by bootstrapping was required for terms to remain in the mortality model. RESULTS Analysis included 549 patients from 15 centers. Hospital mortality was 16% at a median of 16 (1 - 149) days after the Norwood procedure. Shunt type at the end of the Norwood procedure was not a significant risk factor for mortality during the Norwood hospitalization. Independent risk factors for mortality (n=88), renal failure (n=46), sepsis (n=93), length of ventilation among survivors (7 days, range 1 - 270) and hospital LOS among survivors (24 days, range 6 - 270) are shown in the Table. CONCLUSION Innate patient factors, pre-operative condition and lower center volume impact post-operative mortality and morbidity during the Norwood hospitalization.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".