Validation of MagedanzSCORE as a predictor of mediastinitis after coronary artery bypass graft surgery
Bibliographic record
Abstract
"Objective: The aim of this study is to evaluate the applicability of a new score for predicting mediastinitis – MagedanzSCORE – in patients undergoing coronary artery bypass graft (CABG) surgery in the Division of Cardiovascular Surgery of Pronto Socorro Cardiológico de Pernambuco – PROCAPE. Methods: Retrospective study involving 500 patients operated between May/2007 and April/2010. The registers contained all the information used to calculate the MagedanzSCORE. The outcome of interest was mediastinitis. We calculated sensitivity, specificity, positive predictive value, negative predictive value, concordance and accuracy. The accuracy of the model was evaluated by ROC (receiver operating characteristic) curve. Results: The incidence of mediastinitis was 5.6%, with a lethality rate of 32.1%. In univariate analysis, the five variables of the MagedanzSCORE were predictors of postoperative mediastinitis: chronic obstructive pulmonary disease (OR 6.42; 95.0% CI 2.76-14.96; P<0.001), obesity (OR 3.06; 95.0% CI 1.32-7.09; P=0.009), surgical reintervention (OR 82.40; 95.0% CI 30.40-223.30; P<0.001), multiple transfusion (OR 3.33; 95.0% CI 1.52-7.29; P=0.003) and stable angina class IV or unstable (OR 2.59; 95.0% CI 1.19-7.64; P=0.016) according to Canadian Cardiovascular Society. The score had a sensitivity of 96.4%, specificity of 90.0%, positive predictive value of 36.5%, negative predictive value of 99.8% and 90.4% concordance. The accuracy measured by the area under the ROC curve was 96.2% (95.0% CI 94.5%-97.9%). Conclusions: The MagedanzSCORE proved to be a simple and objective index, revealing a satisfactory predictor of development of postoperative mediastinitis in patients undergoing CABG surgery at our institution."
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".