Wound Infection after CABG Using Internal Mammary Artery Grafts: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Internal mammary arteries show better long-term patency rates than venous grafts. The use of both mammary arteries is associated with a higher risk of sternal wound infections. This meta-analysis was designed to assess the incidence of a wound healing disorder after bilateral compared with single mammary artery bypass grafting. Compared with existing meta-analysis this paper includes more current literature and one randomized controlled trial. METHODS: A literature search was performed using PubMed and the Cochrane Library. The quality of the articles was assessed by the Newcastle Ottawa Scale. The odds ratio was used as a measure of the chance of developing a wound healing disorder after bilateral internal mammary artery (BIMA) surgery. Meta-analyses were performed for different subgroups. RESULTS: Twenty studies met the quality criteria, including one randomized controlled trial. The use of both mammary arteries significantly increased the risk of superficial (odds ratio [OR] 1.72) and deep (OR 1.75) wound healing disorder in the total population (OR 1.80) as well as in the diabetic subgroup (OR 1.38) and with both preparation techniques. The increased risk with BIMA grafting was present independently of the preparation technique (pedicled: OR 1.89, skeletonized: OR 1.37). CONCLUSION: Bilateral internal mammary artery grafting, especially in high-risk and diabetic patients, is associated with an increased risk of wound healing impairment. Skeletonized preparation does not eliminate the elevated wound healing disorder risk after BIMA use.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".