Retrograde autologous priming in cardiac surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Guidelines recommend retrograde autologous priming (RAP) of the cardiopulmonary bypass circuit. However, the efficacy and safety of RAP is not well-established. We performed a systematic review and meta-analysis to determine the effects of RAP on transfusion requirements, morbidity and mortality. METHODS: We searched Cochrane Central Register of Controlled Trials, Medline, ScienceDirect, Cumulative Index to Nursing and Allied Health Literature and Embase for randomized controlled trials (RCTs) and observational studies comparing RAP to no-RAP. We performed title and abstract review, full-text screening, data extraction and risk of bias assessment independently and in duplicate. We pooled data using a random effects model. RESULTS: Twelve RCTs (n = 1206) and 17 observational studies (n = 3565) were included. Fewer patients required blood transfusions with RAP [RCTs; risk ratio 0.58 [95% confidence interval (CI): 0.51, 0.65], P < 0.001, and observational studies; risk ratio 0.65 [95% CI: 0.53, 0.80], P < 0.001]. The number of units transfused per patient was also lower among patients who underwent RAP (RCTs; mean difference -0.38 unit [95% CI: -0.72, -0.04], P = 0.03, and observational studies; mean difference -1.03 unit [95% CI: -1.76, -0.29], P < 0.006). CONCLUSIONS: This meta-analysis supports the use of RAP as a blood conservation strategy since its use during cardiopulmonary bypass appears to reduce transfusion requirements.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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, 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".