Effects of percutaneous coronary intervention on dyspnea in stable coronary artery disease
Bibliographic record
Abstract
BACKGROUND: Dyspnea is a frequent symptom in patients with stable coronary artery disease (CAD) and is recognized as a possible angina equivalent. OBJECTIVES: This study was to assess the impact of percutaneous coronary intervention (PCI) on dyspnea, quality of life, and angina pectoris in patients with stable CAD. METHODS: The prospective, multi-center PLA-pCi-EBO-pilot trial included 144 patients with symptomatic stable CAD and successful PCI. The prespecified endpoints angina pectoris (Seattle Angina Questionnaire-SAQ) and dyspnea (NYHA scale) were assessed 6 months after PCI. Predictors for symptomatic improvement were assessed with uni- and multivariable logistic regression analyses. RESULTS: Patients with concomitant dyspnea had worse SAQ physical limitation scores at baseline (49.5 ± 21.0 vs 58.9 ± 22.0, p = 0.013) but showed no difference for angina frequency or quality of life. Overall, symptomatic burden of angina pectoris and dyspnea was alleviated by PCI. However, patients with concomitant dyspnea had markedly worse scores for physical limitation (78.9 ± 25.0 vs 94.3 ± 10.6, p < 0.001), angina frequency (77.9 ± 22.8 vs 91.1 ± 12.4, p < 0.001), and quality of life (69.4 ± 24.1 vs 82.5 ± 14.4, p < 0.001) after PCI. The prevalence of dyspnea (NYHA class ≥ 2) declined from 73% before PCI to 54%. Of 95 initially dyspneic patients, 57 (60%) improved at least one NYHA class 6 months after PCI. In a multivariable logistic regression analysis, "atypical angina pectoris" was associated with improved NYHA class, whereas "diabetes mellitus" had a negative association. CONCLUSION: PCI effectively reduced dyspnea, which is a frequent and demanding symptom in patients with CAD. The German Clinical Trials Register registration number is DRKS0001752 ( www.drks.de ).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".