Aortic versus axillary cannulation in acute type A aortic dissection repair: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Research comparing the effectiveness of central aortic cannulation to axillary artery cannulation in repairing acute type A aortic dissection is limited and controversial. This meta-analysis aimed to compare early outcomes of central aortic cannulation versus axillary artery cannulation for surgery for acute aortic dissection type A. METHODS: A comprehensive systematic search was conducted across PubMed/MEDLINE, Scopus, and the Cochrane Central Register of Controlled Trials up to September 1, 2023. The primary endpoints were operative mortality and incidence of postoperative stroke. Secondary endpoints encompassed cardiopulmonary bypass time, myocardial ischemic time, hypothermic circulatory arrest time, postoperative temporary neurological dysfunction, combination of stroke and temporary neurological dysfunction, as well as the need for reexploration for bleeding, renal replacement therapy, and tracheotomy. A random-effect model was utilized to calculate the pooled effect size. RESULTS: Eleven studies met our eligibility criteria, enrolling a total of 7204 patients (2760 underwent aortic cannulation and 4444 underwent axillary cannulation). The operative mortality and incidence of postoperative stroke did not show statistical differences between the two groups, with a pooled odds ratio of 1.07 (95% confidence interval: 0.73-1.55) and 1.17 (0.95-1.42), respectively. Similarly, none of the secondary endpoints exhibited significant statistical differences between the two groups. CONCLUSIONS: Aortic cannulation can be a viable alternative to axillary artery cannulation for repair of acute aortic dissection type A, as both approaches present similar early clinical outcomes.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".