Thorax support vest to prevent sternal wound infections in cardiac surgery patients—a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Midline sternotomy is the main surgical access for cardiac surgeries. The most prominent complication of sternotomy is sternal wound infection (SWI). The use of a thorax support vest (TSV) that limits thorax movement and ensures sternal stability has been suggested to prevent postoperative SWI. METHODS: We performed a meta-analysis to evaluate differences in clinical outcomes with and without the use of TSV after cardiac surgery in randomized trials. The primary outcome was deep SWI (DSWI). Secondary outcomes were superficial SWI, sternal wound dehiscence, and hospital length of stay (LOS). A trial sequential analysis was performed. Fixed (F) and random effects (R) models were calculated. RESULTS: A total of 4 studies (3820 patients) were included. Patients who wore the TSV had lower incidence of DSWI [odds ratio (OR) = F: 0.24, 95% confidence interval (CI), 0.13-0.43, P < 0.01; R: 0.24, 0.04-1.59, P = 0.08], sternal wound dehiscence (OR = F: 0.08, 95% CI, 0.02-0.27, P < 0.01; R: 0.10, 0.00-2.20, P = 0.08) and shorter hospital LOS (standardized mean difference = F: -0.30, -0.37 to -0.24, P < 0.01; R: -0.63, -1.29 to 0.02, P = 0.15). There was no difference regarding the incidence of superficial SWI (OR = F: 0.71, 95% CI, 0.34-1.47, P = 0.35; R: 0.64, 0.10, 4.26, P = 0.42). The trial sequential analysis, however, showed that the observed decrease in DSWI in the TSV arm cannot be considered conclusive based on the existing evidence. CONCLUSIONS: This meta-analysis suggests that the use of a TSV after cardiac surgery could potentially be associated with a reduction in sternal wound complications. However, despite the significant treatment effect in the available studies, the evidence is not solid enough to provide strong practice recommendations.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".