Bibliographic record
Abstract
OBJECTIVE: Risk scores have become an important tool in patient assessment, as age, severity of heart disease, and comorbidity in patients undergoing heart surgery have considerably increased. Various risk scores have been developed to predict mortality after heart surgery. However, there are significant differences between scores with regard to score design and the initial patient population on which score development was based. It was the purpose of our study to compare six commonly used risk scores with regard to their validity in our patient population. METHODS: Between September 1, 1998 and February 28, 1999, all adult patients undergoing heart surgery with cardiopulmonary bypass in our institution were preoperatively scored using the initial Parsonnet, Cleveland Clinic, French, Euro, Pons, and Ontario Province Risk (OPR) scores. Postoperatively, we registered 30-day mortality, use of mechanical assist devices, renal failure requiring hemodialysis or hemofiltration, stroke, myocardial infarction, and duration of ventilation and intensive care stay. Score validity was assessed by calculating the area under the ROC curve. Odds ratios were calculated to investigate the predictive relevance of risk factors. RESULTS: Follow-up was able to be completed in 504 prospectively scored patients. Receiver operating characteristics (ROC) curve analysis for mortality showed the best predictive value for the Euro score. Predictive values for morbidity were considerably lower than predictive values for mortality in all of the investigated score systems. For most risk factors, odds ratios for mortality were substantially different from ratios for morbidity. CONCLUSIONS: Among the investigated scores, the Euro score yielded the highest predictive value in our patient population. For most risk factors, predictive values for morbidity were substantially different from predictive values for mortality. Therefore, development of specific morbidity risk scores may improve prediction of outcome and hospital cost. Due to the heterogeneity of morbidity events, future score systems may have to generate separate predictions for mortality and major morbidity events.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".