Pulsatile Versus Nonpulsatile Cardiopulmonary Bypass Flow: An Evidence-Based Approach
Bibliographic record
Abstract
OBJECTIVE: To derive evidence-based recommendations for the use of pulsatile perfusion (PP) technique for the reduction of mortality and nonfatal complications after elective coronary artery bypass grafting surgery (CABG). OUTCOMES: Incidence of total mortality, myocardial infarction (MI), stroke, and renal failure during hospital stay. EVIDENCE: Medline, Embase, and the Cochrane controlled trial register (CCTR) on the Cochrane library were searched from the earliest achievable date of each database to March 2005. No language restrictions were applied. Retrieved reprints were evaluated according to a priori inclusion criteria, and those included were critically appraised using established internal validity criteria. BENEFITS AND HARMS: Only one fair quality randomized controlled trial demonstrated the beneficial effect of PP in reducing the incidence of total mortality and MI. No studies demonstrated the beneficial effect of PP in reducing the incidence of stoke or renal failure. One randomized controlled trial demonstrated that PP was associated with increased hemolysis compared to nonpulsatile (NP) perfusion. CONCLUSION: The evidence is conflicting and therefore does not support making recommendation for or against routinely providing the PP to reduce the incidence of mortality or MI. The evidence is insufficient to recommend for or against routinely providing the pulsatile profusion to reduce the incidence of stroke or renal failure.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".