Outcomes in Patients with Acute and Stable Coronary Syndromes; Insights from the Prospective NOBORI-2 Study
Bibliographic record
Abstract
BACKGROUND: Contemporary data remains limited regarding mortality and major adverse cardiac events (MACE) outcomes in patients undergoing PCI for different manifestations of coronary artery disease. OBJECTIVES: We evaluated mortality and MACE outcomes in patients treated with PCI for STEMI (ST-elevation myocardial infarction), NSTEMI (non ST-elevation myocardial infarction) and stable angina through analysis of data derived from the Nobori-2 study. METHODS: Clinical endpoints were cardiac mortality and MACE (a composite of cardiac death, myocardial infarction and target vessel revascularization). RESULTS: 1909 patients who underwent PCI were studied; 1332 with stable angina, 248 with STEMI and 329 with NSTEMI. Age-adjusted Charlson co-morbidity index was greatest in the NSTEMI cohort (3.78±1.91) and lowest in the stable angina cohort (3.00±1.69); P<0.0001. Following Cox multivariate analysis cardiac mortality was independently worse in the NSTEMI vs the stable angina cohort (HR 2.31 (1.10-4.87), p = 0.028) but not significantly different for STEMI vs stable angina cohort (HR 0.72 (0.16-3.19), p = 0.67). Similar observations were recorded for MACE (<180 days) (NSTEMI vs stable angina: HR 2.34 (1.21-4.55), p = 0.012; STEMI vs stable angina: HR 2.19 (0.97-4.98), p = 0.061. CONCLUSIONS: The longer-term Cardiac mortality and MACE were significantly worse for patients following PCI for NSTEMI even after adjustment of clinical demographics and Charlson co-morbidity index whilst the longer-term prognosis of patients following PCI STEMI was favorable, with similar outcomes as those patients with stable angina following PCI.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".