Comparison of various score systems for risk stratification in heart surgery
Bibliographic record
Abstract
Introduction Its is important to predict patients having highest risk of surgery. Risk stratification systems need to be tested in different surgical populations and whether they are or not appropriate to our population remains unknown. Objectives To test various risk stratification systems for our region population having cardiac surgery in our institution during 2002. Materials and methods Between January 1, 2002 and November 1, 2002, all adult patients undergoing heart surgery with cardiopulmonary bypass in our institution were included in the study and scored using the EuroSCORE, Parsonnet, Ontario, and QMMI. Study was completed in 444 patients. We analysed score systems predicting characteristics by assessing receiver operating characteristics (ROC). Results Observed mortality was 25 (5,63 %). Mean score for alive and dead patients for EuroSCORE was -7,8±3,1 and 10,8±3,2, p < 0,005; Parsonnet - 14,2±11 and 32,5±13,8, p < 0,0005; Ontario - 3,6±2,7 and 6,4±3,5, p < 0,005; QMMI score - 10,4±6,9 and 20,3±8,7, p < 0,0001. ROC curve analysis for mortality showed best predicting characteristics for the Parsonnet and QMMI, best accuracy for QMMI score -84,4 %. Conclusions Most (71.2 %) of our investigated patients having heart surgery are at high-risk group for death. All investigated score systems have significance in mortality prediction. Among the investigated score systems, the QMMI score and Ontario score systems yielded the highest predictive value in our patient population. Highest accuracy of prediction patient population showed QMMI score. Our study highlighted over prediction of mortality for Parsonnet score and EuroSCORE systems for our population.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".