Routine use of glycoprotein IIb/IIIa inhibitor therapy is associated with an improved in-hospital outcome after percutaneous coronary intervention: Insights from a large, prospective, single-centre registry.
Bibliographic record
Abstract
BACKGROUND: Clinical trials have demonstrated the benefit of glycoprotein IIb/IIIa inhibitors (GPIs) with stenting in the setting of percutaneous coronary intervention (PCI) in reducing adverse cardiac outcomes. OBJECTIVES: To assess the association between a strategy of routine GPI use and the prevention of adverse cardiac outcomes post-PCI at a large, tertiary care institution. PATIENTS AND METHODS: Patients who underwent a first PCI at the institution from April 1994 to February 2001 were divided into three cohorts: pre-GPI with selected stent use (P1), selected GPI with routine stent use (P2), and routine GPI with routine stent use (P3). A multiple logistic regression model was constructed to evaluate the different strategy periods, with the rate of in-hospital myocardial infarction, death and abrupt closure as the primary composite end point. RESULTS: The cohort comprised 7702 patients (2621, 3501 and 1580 patients in P1, P2 and P3, respectively). GPI use increased from 0% in P1 to 17.5% in P2 to 91.0% in P3. There was a significant difference in the unadjusted composite end point among the three periods (4.9%, 3.4% and 2.6%, for P1, P2 and P3, respectively; P<0.001). In the multiple regression analysis, the composite end point was significantly greater in P1 and P2 than in P3 (P1 versus P3, OR 2.61, P<0.0001; P2 versus P3, OR 1.55; P=0.0204). CONCLUSIONS: A significant reduction in adverse in-hospital clinical cardiac events was observed in association with the strategy of routine use of GPI therapy and stenting in a large, consecutive PCI cohort after adjusting for potentially confounding patient characteristics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".