Direct aortic cannulation versus femoral arterial cannulation for early outcomes in acute type A aortic dissection: A study-level meta-analysis
Bibliographic record
Abstract
PURPOSE: To investigate the impact of direct aortic cannulation (DAC) versus femoral arterial cannulation (FAC) on clinical outcomes of surgery for acute type A aortic dissection. METHODS: PubMed/MEDLINE, Scopus, and Cochrane Central Register of Controlled Trials were searched until August 25, 2023, to conduct a meta-analysis. Primary endpoints of the study were operative mortality and postoperative stroke. Secondary endpoints were cardiopulmonary bypass time, myocardial ischemic time, hypothermic circulatory arrest time, temporary neurological dysfunction (TND), combined stroke and TND, re-exploration for bleeding, and need for renal replacement therapy. A random-effect model was used to estimate the pooled effect size, and a leave-one-out method was used for the primary endpoints for sensitivity analysis. RESULTS: 15 studies met our eligibility criteria, including a total of 7941 samples. Operative mortality was significantly lower in the DAC group with a pooled odds ratio (OR) of 0.72 [95% confidence interval (CI): 0.61-0.85)]. Incidence of postoperative stroke was also lower in the DAC group with a pooled OR of 0.79 (95% CI: 0.66-0.94). However, after excluding one study with the greatest weight, the difference became nonsignificant. DAC was also associated with a lower incidence of postoperative TND, and re-exploration for bleeding with a pooled OR of 0.52 (95% CI: 0.37-0.73), and 0.60 (95% CI: 0.47-0.77), respectively. CONCLUSIONS: This meta-analysis showed that patients who underwent ATAAD repair with DAC had a lower incidence of operative mortality, postoperative stroke, TND, and re-exploration for bleeding compared to those who underwent FAC.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.063 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".