Thrombin-activatable fibrinolysis inhibitor (TAFI): a novel predictor of angiographic coronary restenosis
Bibliographic record
Abstract
The fibrinolytic system is closely related to several processes that are involved in restenosis. We previously showed that low PAI-1 plasma levels predicted restenosis. Recently, a different fibrinolytic inhibitor, TAFI, has been described. The aims of this study were to evaluate the relationship between pre-procedural plasma levels of TAFI and late angiographic restenosis and the interaction between TAFI and PAI-1.We prospectively studied 159 patients with stable angina who underwent successful elective angioplasty or stenting of de novo native coronary artery lesions. TAFI and PAI-1 antigen levels were measured in plasma samples drawn before the procedure. Follow-up coronary angiography was performed in 92% of patients. There was a significant correlation between pre-procedural TAFI levels and 6-month % diameter stenosis (DS) (r = 0.21; p = 0.013). The overall angiographic restenosis rate (DS>50%) was 31%. Pre-procedural TAFI levels were significantly higher in patients with restenosis (108 +/- 33% versus 94+/-30%, p = 0.011). Restenosis rates for patients in the upper tertile of TAFI levels were 2-fold higher than for those in the lowest tertile (45% versus 22%; p = 0.016). A combination of high TAFI and low PAI-1 levels identified patients at the highest risk of restenosis (53%) compared to 14% in patients with low TAFI and high PAI-1 levels; p = 0.027. In conclusion, pre-procedural plasma TAFI antigen levels identify patients at increased risk for restenosis after PCI.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".