Validação do MagedanzSCORE como preditor de mediastinite após cirurgia de revascularização miocárdica
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".