Serum total bilirubin and long-term outcome in patients undergoing percutaneous coronary intervention
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the associated between serum total bilirubin (STB) levels and long-term outcomes in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI). METHODS: A total of 1,273 consecutive patients were enrolled. Patients were grouped according to their baseline STB levels: Group 1 (STB < 3.4 μmol/L), Group 2 (3.4 μmol/L ≤ STB ≤ 10.3 μmol/L), Group 3 (10.3 μmol/L < STB ≤ 17.1 μmol/L), and Group 4 (STB < 17.1 μmol/L) and the rate of major adverse cardiovascular events (MACE) was determined RESULTS: A total of 1,152 patients were successfully followed up (90.5%) for a mean period of 30 ± 5 months, including 187 patients experiencing a major adverse cardiovascular event (MACE: death from any cause, myocardial infarction, repeat revascularization or readmission). The MACE rate in Groups 3 and 4 was lower than in Groups 1 and 2 (P < 0.01). After adjusted the confounding factors with Cox regression analysis, the MACE rates in Groups 2-4 were still lower than in Group 1 (Group 2, RR=0.293, 95% CI 0.167-0.517, P < 0.01; Group 3, RR=0.142, 95% CI 0.065-0.312, P < 0.01; Group 4, RR=0.134, 95% CI 0.071-0.252, P < 0.01). The cumulative survival rates of Groups 3 and 4 were higher than that of Groups 1and 2 (P < 0.01). CONCLUSIONS: High STB concentration is associated with lower MACE in patients with ACS after PCI.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".