Fractional flow reserve use in coronary artery revascularization: A systematic review and meta-analysis
Bibliographic record
Abstract
Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is recommended in revascularization guidelines for intermediate lesions. However, recent studies comparing FFR-guided PCI with non-physiology-guided revascularization have reported conflicting results. PubMed and Embase were searched for studies comparing FFR-guided PCI with non-physiology-guided revascularization strategies (angiography-guided, intracoronary imaging-guided, coronary artery bypass grafting). Data were pooled by meta-analysis using random-effects model. 26 studies enrolling 78,897 patients were included. FFR-guided PCI as compared to non-physiology-guided coronary revascularization had lower risk of all-cause mortality (odds ratio [OR] 0.79 95% confidence interval [CI] 0.64–0.99, I 2 = 53%) and myocardial infarction (MI) (OR 0.74 95% CI 0.59–0.93, I 2 = 44.7%). However, no differences between groups were found in terms of major adverse cardiac events (MACEs) (OR 0.86 95% CI 0.72–1.03, I 2 = 72.3%) and repeat revascularization (OR 1 95% CI 0.82–1.20, I 2 = 43.2%). Among patients with coronary artery disease (CAD), FFR-guided PCI as compared to non-physiology-guided revascularization was associated with a lower risk of all-cause mortality and MI.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".