Predictors of angina resolution after percutaneous coronary intervention in stable coronary artery disease
Bibliographic record
Abstract
BACKGROUND: Elective percutaneous coronary intervention (PCI) is performed to relieve symptoms of angina. Identifying patients who will benefit symptomatically after PCI would be clinically advantageous but robust predictors of symptom resolution are ill-defined. METHODS: Prospective indexing of baseline angina status, clinical, and procedural characteristics were collected over a 5-year period in a regional revascularization registry. At 1-year follow-up, angina resolution was assessed. We performed a stepwise selection algorithm to identify predictors of persistent angina at 1 year. RESULTS: A total of 777 patients were included in the analysis and the median follow-up was 387 days. Mean age of the cohort was 66.6 years, 23.8% were female and 23.3% had baseline Canadian Cardiovascular Society class 3 or 4 angina. Overall, 13.1% had persistent angina. The only predictor of persistent angina was the presence of a residual chronic total occlusion after PCI with odds ratio of 3.06 (95% confidence interval, 1.81-5.17). Residual stenoses 50-69%, 70-89%, and 90-99% were not associated with residual angina after PCI. CONCLUSION: Most patients achieved symptom resolution with PCI and optimal medical therapy. A residual chronic total occlusion after PCI was associated with persistent angina. Other degrees of stenoses were not associated with persistent angina.
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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 | 0.005 |
| 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 | 0.001 | 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".