Paclitaxel Versus Sirolimus Stents in Diabetic and Nondiabetic Patients
Bibliographic record
Abstract
BACKGROUND: Drug-eluting stents are more effective in reducing restenosis than bare-metal stents. Less certain is the relative performance of 2 widely used drug-eluting stents-sirolimus- and paclitaxel-eluting stents-in diabetic and nondiabetic patients undergoing percutaneous coronary intervention in routine clinical practice. We therefore studied the long-term effectiveness and safety of sirolimus versus paclitaxel stents overall and stratified by the absence or presence of diabetes. METHODS AND RESULTS: We compared sirolimus and paclitaxel stents in a propensity-score matched cohort of 2054 pairs of patients (835 matched pairs of diabetic patients and 1219 matched pairs of nondiabetic patients) undergoing percutaneous coronary intervention in Ontario between December 1, 2003 and March 31, 2006. The cohort was derived from the Cardiac Care Network of Ontario percutaneous coronary intervention registry and linked to population-based administrative health databases. In the overall cohort, there was no difference in rates of target-vessel revascularization (P=0.47), myocardial infarction (P=0.71), or death (P=0.49). As compared with paclitaxel stents, the use of sirolimus stents was associated with a significantly lower 3-year rate of target-vessel revascularization in nondiabetic patients (8.3% versus 10.0%, P=0.01), but not in diabetic patients (12.7% versus 10.3%, P=0.07). Rates of all-cause mortality were similar in patients receiving sirolimus stents versus paclitaxel stents in both the diabetic (8.4% versus 9.2%, P=0.91) and nondiabetic (4.6% versus 3.0%, P=0.22) groups. CONCLUSIONS: In this large observational study, patients receiving paclitaxel and sirolimus stents had similar mortality rates, but nondiabetic patients receiving sirolimus stents were significantly less likely to require repeat revascularization.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".