Impact of Sex on Residual Angina After Percutaneous Coronary Interventions
Bibliographic record
Abstract
BACKGROUND: Sex-related differences in the clinical presentation of coronary artery disease (CAD) are well recognized. The pullback pressure gradient (PPG) characterizes pathophysiological CAD patterns as focal or diffuse. OBJECTIVES: To evaluate the influence of sex on residual angina at one year after percutaneous coronary intervention (PCI), stratified by PPG. METHODS: We performed a sub-analysis of PPG Global, a multicenter, prospective, single-arm study. All patients had hemodynamically significant CAD (fractional flow reserve [FFR] ≤ 0.80) and underwent a manual FFR pullbacks to calculate PPG before PCI. Patient-reported outcomes were collected using the 7-item Seattle Angina Questionnaire (SAQ-7) at baseline and 1-year follow-up. RESULTS: We included 814 patients (205 [25.2%] women and 609 [74.8%] men). Women were significantly older than men (70 ± 10 years vs. 67 ± 10 years p < 0.001). Baseline FFR were comparable between sexes (0.68 ± 0.13 vs. 0.66 ± 0.12, p = 0.098), but women had a more focal CAD compared to men (PPG 0.66 ± 0.15 vs. 0.63 ± 0.15, p = 0.047). Post PCI-FFR was higher in women than men (0.88 ± 0.07 vs. 0.87 ± 0.07, p = 0.041); however, angina at 1 year was more frequent in women (SAQ angina frequency score 94 ± 12 vs. 96 ± 10, p = 0.041). CONCLUSION: Despite having a more focal CAD pattern and achieving higher post-PCI FFR, women report more residual angina than men at 1-year follow-up. TRIAL REGISTRATION: ClinicalTrials.gov NCT04789317.
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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".