EURO-SCORING FOR RISK STRATIFICATION IN CABG SURGERY - 10 YEARS EXPERIENCE
Bibliographic record
Abstract
Aim of the study was to evaluate patients pre operatively with Euroscoring System to judge the end point, hospital mortality in CABG done in last ten years by a single surgeon. From June 1989 to June 1999 the data of 262 cases of CABG done was collected on the data sheet each case was scored pre operatively with Euroscoring System. The different groups were made from this system, Group 1 Score 0-1, Group 2 Score 3- 4, Group 3 Score 5-6, Group 4 Score 7-8, Group 5 > 9, Another Grouping was Group 1 score 0-5, and Group 2 score 6-10 and Group 3 score 11-15. Pre op data was collected and analyzed by SPSS Version 7.5, The End point was hospital mortality. In this group of 262 patients, the age range was 25-77 with the mean 52.41 years, 248 (94.7%) were male and 14 (5.3%) were female. In this whole cohort of pas patients 227 (86.6%) were having stable angina pectoris and 35 (13.4%) were having unstable angina. Pre op angina status was Class I in 5 (1.9%), Class II 88 (33,6%), Class III 132 (50.4%) and Class IV were 37 (14.1%). There were 116 (44.3%) hypertensive, 56 (21.4%) were diabetics and 9 (3.4%) were obese. Recent myocardial Infarction was there in 9 (14%) of cases, the old non Q- wave infarction was present in 18 (6.9%) of cases and Q- wave infarction was present in 42 (16%) of cases. Pre op Ejection fraction was good in (EF>50%) in 204 (77.9%) cases, Fair EF 30-49%) in 50 (19.1%) cases, poor EF 9) the mortality was 60%. The Euroscore from 0-5 was having 6.1% mortality, the score from 6-10 was having 20% and the score from 11-15 was having 80% mortality. On Logistic regression overall predictive accuracy of Euroscoring is very good (90%), Predictive accuracy, 37% of deaths could be explained on the existing variables positive predictive value is 19.05% and negative predictive value is 99.17%. The predictive accuracy of Euroscoring changes with various risk groups. In low risk Groups (Score 0-5) and (Score 6-10) Euroscore predicts survival more accurately. In high risk Group (11-15) Euroscoring better predicts mortality rather than survival. The factors included in permutations of Euroscore explain only 37% of the observed mortality. It is noted that the observed mortality is consistently higher than that predicted by logistic regression. Euroscoring is a good tool of risk stratification to predict the out come but not ideally suited to our clinical circumstances. Though we have documented an overall predictive accuracy of 92%, it is limited in its usefulness because it does not take into consideration certain risk factors found to be important in our patient population. In addition, the relative weight assigned to various risk factors in scoring needs to be readjusted for our patient population in the light of observations made on our patient population. There is a need to develop a scoring system of our own which could be used for better prediction of outcomes in our clinical circumstances
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".