Validation and Comparison of Six Risk Scores for Infection in Patients With ST-Segment Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention
Bibliographic record
Abstract
Aims: Very few of the risk scores to predict infection in ST-segment elevation myocardial infarction (STEMI) patients undergoing percutaneous coronary intervention (PCI) have been validated, and reports on their differences. We aimed to validate and compare the discriminatory value of different risk scores for infection. Methods: A total of 2,260 eligible patients with STEMI undergoing PCI from January 2010 to May 2018 were enrolled. Six risk scores were investigated: age, serum creatinine, or glomerular filtration rate, and ejection fraction (ACEF or AGEF) score; Canada Acute Coronary Syndrome (CACS) risk score; CHADS 2 score; Global Registry for Acute Coronary Events (GRACE) score; and Mehran score conceived for contrast induced nephropathy. The primary endpoint was infection during hospitalization. Results: Except CHADS 2 score (AUC, 0.682; 95%CI, 0.652–0.712), the other risk scores showed good discrimination for predicting infection. All risk scores but CACS risk score (calibration slope, 0.77; 95%CI, 0.18–1.35) showed best calibration for infection. The risks scores also showed good discrimination for in-hospital major adverse clinical events (MACE) (AUC range, 0.700–0.786), except for CHADS 2 score. All six risk scores showed best calibration for in-hospital MACE. Subgroup analysis demonstrated similar results. Conclusions: The ACEF, AGEF, CACS, GRACE, and Mehran scores showed a good discrimination and calibration for predicting infection and MACE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".