Preoperative Intraaortic Balloon Pump Improves Early Outcomes Following High-Risk Coronary Artery Bypass Graft Surgery: A Meta-Analysis of Randomized Trials and Prospective Study Design.
Bibliographic record
Abstract
BACKGROUND: Despite widespread use, evidence to support preemptive intraaortic balloon pump (IABP) insertion for patients undergoing high-risk coronary artery bypass graft (CABG) surgery remains sparse, and in need of a well-defined clinical trial. To inform the design of a prospective trial, we sought to review outcomes in randomized controlled trials (RCTs) of anticipatory IABP use vs control in patients undergoing high-risk CABG through meta-analysis. The primary endpoint was all-cause mortality within 30 days of surgery. The secondary endpoint was major adverse cardiac and cerebrovascular event (MACCE), a composite of death, myocardial infarction, stroke, or repeat revascularization. METHODS: Using Ovid MEDLINE, we systematically reviewed all RCTs comparing preoperative IABP with control in patients undergoing high-risk CABG, defined as: left ventricular ejection fraction (LVEF) ≤40%, left main stenosis ≥70%, unstable angina, recent myocardial infarction, or prior myocardial revascularization undergoing elective or emergent CABG on or off pump. RESULTS: Of 950 articles assessed for eligibility, 10 RCTs of 1261 subjects (mean age, 65.0 years; 21.8% women; mean LVEF, 35%) were included. Mortality was significantly lower in patients receiving IABP compared with control (relative risk [RR], 0.48; 95% confidence interval [CI], 0.30-0.76; P<.01). The risk of MACCE was also lower with IABP (RR, 0.67; 95% CI, 0.54-0.84; P<.001). No significant differences in major bleeding events (RR, 1.27; 95% CI, 0.44-3.72) or vascular complications (RR, 1.13; 95% CI, 0.42-3.06) were detected. CONCLUSIONS: A strategy of routine prophylactic IABP use may reduce short-term mortality and MACCE in high-risk CABG patients. A definitive, adequately powered, prospective, randomized trial is warranted to confirm these results.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".