Evaluation of the role of abciximab (Reopro) as a rescue agent during percutaneous coronary interventions: In-hospital and six-month outcomes
Bibliographic record
Abstract
Abciximab is effective for the prevention of complications when administered prior to percutaneous coronary intervention (PCI). The efficacy and safety of abciximab as an unplanned or rescue agent for complications of PCI is unknown. Rescue versus planned use was compared in 186 consecutive patients. Primary or rescue PCI for acute myocardial infarction (MI) and shock were excluded. Rescue abciximab use was undertaken in 101 patients (54.3%) and planned abciximab was used in 85 (45.7%). The rescue abciximab patients had a lower incidence of previous MI, preprocedural thrombus, multivessel, and vein graft intervention. In-hospital endpoints in the rescue versus planned abciximab patients were death (1.0% vs. 1. 2%, P = 1.0), Q-wave MI (2.0% vs. 2.4%, P = 1.0), any MI (14.9% vs. 9.4%, P = 0.3), target vessel revascularization (TVR; 0% vs. 1.2%, P = 1.0), and composite (15.8% vs. 10.6%, P = 0.3). At 6 months, events were death (4.0% vs. 2.3%, P = 0.69), MI (14.9% vs. 9.4%, P = 0.26), TVR (20.8% vs. 4.7%, P = 0.001), and composite (30.7% vs. 15. 3%, P = 0.01). In-hospital complications between the rescue and planned abciximab patients of major bleed (1.0% vs. 1.8%, P = NS), stroke (0% vs. 1.8%, P = NS), and thrombocytopenia (3.0% vs. 1.8%, P = NS) were similar. There was a significantly higher procedural time (99.6 min vs. 86.1 min, P = 0.02), contrast volume (278.8 ml vs. 223. 5 ml, P = 0.04), and heparin use (8984 u vs. 6003 u, P = 0.0006) in the rescue group. In this nonrandomized comparison, rescue abciximab allowed for the safe discharge from hospital in the majority of patients. However, during a 6-month follow-up, more patients treated with rescue abciximab required TVR with either repeat PCI or CABG. Further studies are warranted to evaluate the overall strategy of rescue abciximab use in 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".