The Value of Pre- and Post-Stenting Fractional Flow Reserve for Predicting Mid-Term Stent Restenosis Following Percutaneous Coronary Intervention (PCI)
Bibliographic record
Abstract
Measuring fractional flow reserve (FFR) in percutaneous coronary intervention (PCI) has predictive value for PCI outcome. We decided to examine the utility of pre- and post-stenting FFR as a predictor of 6-month stent restenosis as well as MACE (major adverse cardiac events). Pre- and post-stenting FFR values were measured for 60 PCI patients. Within 6 months after stenting, all patients were followed for assessment of cardiac MACE including myocardial infarction, unstable angina, or positive exercise test. Stent restenosis was also assessed. Cut-off values for pre- and post-stenting FFR measurements were considered respectively as 0.65 and 0.92.Stent restenosis was detected in 4 patients (6.6%). All 4 patients (100%) with restenosis had pre-stenting FFR of < 0.65, while only 26 of 56 patients without restenosis (46.4%) had pre-stenting FFR value of < 0.65 (P= 0.039). Mean pre-stenting FFR in patients with restenosis was significantly lower than in those without restenosis (0.25 ± 0.01 vs. 0.53 ± 0.03, P= 0.022). Although stent restenosis was higher in patients with post-stenting FFR of < 0.92 (2 cases, 9.5%) than in those with FFR value of ≥ 0.92 (2 cases, 5.1%), the difference was not statistically (P= 0.510). Pre-stenting FFR, the use of longer stents, and history of diabetes mellitus can predict stent restenosis, but the value of post-stenting FFR for predicting restenosis was not explicit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".