Systemic Nanoparticle Paclitaxel (<i>nab</i>‐Paclitaxel) for In‐stent Restenosis I (SNAPIST‐I): A First‐in‐Human Safety and Dose‐finding Study
Bibliographic record
Abstract
BACKGROUND: Paclitaxel-eluting stents inhibit restenosis; however, this technology has drawbacks (e.g., stent thrombosis, requirement for long-term antiplatelet therapy, and cost--particularly for patients with multivessel disease). Systemic treatment with a novel 130-nm, albumin-bound particle form of paclitaxel (nab-paclitaxel) has been shown to reduce restenosis in animals. HYPOTHESIS: This study was designed to establish the safety and optimal dose of systemic nab-paclitaxel for reducing in-stent restenosis in humans. If well tolerated, systemic nab-paclitaxel may be used with any available bare-metal stent and at potentially lower cost than drug-eluting stents. METHODS: Patients received nab-paclitaxel 10, 30, 70, or 100 mg/m(2) intravenously after stenting of a single de novo lesion >or= 3 mm in diameter. Study endpoints included safety and major adverse cardiac events (MACE) at 2 and 6 months. RESULTS: Data were obtained for all 23 enrolled patients (mean age 66 +/- 10 years, 74% men, 26% with diabetes). No significant adverse events (AE) were attributable to nab-paclitaxel at 10 or 30 mg/m(2). Moderate neutropenia, moderate sensory neuropathy, and mild to moderate, reversible alopecia occurred only at doses of 70 and 100 mg/m(2); therefore, doses of 70 mg/m(2) or higher were considered unacceptable in this patient population. No MACE were reported at 2 months. At 6 months, 4 target lesion revascularizations (TLR) for restenoses were reported (2 each in the 10- and 100-mg/m(2)-dose groups). CONCLUSIONS: Systemic nab-paclitaxel was well tolerated at doses below 70 mg/m(2) in this group of patients; no unexpected AE were noted. Additional studies are under way to explore intravenous and intracoronary administration of nab-paclitaxel.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".