Fractional-flow reserve use in coronary artery revascularization: a 78.897 patients systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is recommended in the guidelines of revascularization for patients presenting with coronary artery disease (CAD) with intermediate lesion severity. However, recently published studies comparing FFR-guided PCI with non-physiology guided revascularization strategies have reported conflicting result. Methods The PubMed and Embase databases were searched for randomized clinical trials and observational studies comparing FFR-guided PCI with non-physiology-guided revascularization strategies (angiography-guided, intracoronary imaging-guided, coronary artery bypass grafting) in patients with CAD. Data were pooled by meta-analysis using a random-effects model. Results 26 studies enrolling 78,897 patients were included. Patients undergoing FFR-guided PCI as compared to those undergoing non-physiology guided coronary revascularization had a lower risk of all-cause mortality (OR 0.79 95% CI 0.64-0.99, I2=53%) and myocardial infarction (OR 0.74 95% CI 0.59-0.93, I2=44.7%). However, no differences between groups were found in terms of MACE (OR 0.86 95% CI 0.72-1.03, I2=72.3%) and repeat revascularization (OR 1 95% CI 0.82-1.20, I2=43.2%). Conclusions Among patients with CAD, FFR-guided PCI as compared to non-physiology-guided revascularization was associated with a lower risk of all-cause mortality and MI.Clinical outcomes in patients undergoingFFR-guided PCI as compared to non-physio
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.005 | 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) | 0.005 | 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".