Canada Acute Coronary Syndrome Score: A Preprocedural Risk Score for Contrast-Induced Nephropathy After Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
In patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention, contrast-induced nephropathy (CIN) is a serious complication associated with poor outcomes. We assessed the predictive value of the Canada Acute Coronary Syndrome (C-ACS) score for CIN in these patients. A total of 394 consecutive patients with STEMI were enrolled and divided into 3 groups according to their C-ACS scores-group 1, score 0; group 2, score 1; and group 3, score ≥2. The clinical outcomes were CIN and major adverse clinical events (MACEs) during hospital and follow-up; 8.4% of patients developed CIN. Patients with high C-ACS scores were more likely to develop CIN, in-hospital death, and MACEs ( P < .001). The C-ACS score was an independent predictor of CIN (odds ratio = 2.87; 95% confidence interval = 1.78-4.63; P < .001) and risk factor for long-term MACEs. The C-ACS score had good predictive values for CIN, in-hospital morality, MACEs, and long-term mortality. Patients with high C-ACS risk scores exhibited a worse survival rate than those with low scores (death, P = .02; MACEs, P = .006). In conclusion, in patients with STEMI, the C-ACS could predict CIN and clinical outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".