Effects of AGI-1067 and Probucol After Percutaneous Coronary Interventions
Bibliographic record
Abstract
BACKGROUND: AGI-1067, a metabolically stable modification of probucol, is an equipotent antioxidant to probucol but is pharmacologically distinct. In a multicenter trial, we studied whether AGI-1067 reduces restenosis assessed by intravascular ultrasound (IVUS) after percutaneous coronary intervention (PCI) compared with placebo and probucol used as a positive control. METHODS AND RESULTS: Two weeks before PCI, 305 patients were randomly assigned to 1 of 5 treatment groups: placebo, probucol 500 mg BID, or AGI-1067 70, 140, or 280 mg once daily. Patients were treated for 2 weeks before and 4 weeks after PCI. Baseline and 6-month follow-up IVUS were interpreted by a blinded core laboratory. Stents were used in 85% of patients. Luminal area at the PCI site at follow-up was 2.66+/-1.58 mm2 for placebo, 3.69+/-2.69 mm2 for probucol, 2.75+/-1.76 mm2 for AGI-1067 70 mg, 3.17+/-2.26 mm2 for AGI-1067 140 mg, and 3.36+/-2.12 mm2 for AGI-1067 280 mg (P=0.02 for the dose-response relationship; P< or =0.05 for AGI-1067 280 mg and probucol versus placebo). There was a mean narrowing of 5.3 mm3 of reference segment lumen in the placebo group and an enlargement in the AGI-1067 140- and 280-mg groups at follow-up (P=0.05 for 140 mg). An increase in QTc interval >60 ms occurred in 4.8% of placebo patients, 17.4% of probucol patients, and 4.8%, 2.4%, and 2.5% of patients in the AGI-1067 groups (P=0.02). CONCLUSIONS: AGI-1067 and probucol reduce restenosis after PCI. In contrast to probucol, AGI-1067 did not cause prolongation of the QTc interval and improved lumen dimensions of reference segments, suggestive of a direct effect on atherosclerosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".