RETRACTED: Risk factors associated with surgical site infections in patients undergoing cardiothoracic surgery: A systematic review and meta‐analysis
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Abstract Surgical site infections (SSIs) following cardiothoracic surgery can pose significant challenges to patient recovery and outcome. This systematic review and meta‐analysis aim to identify and quantify the risk factors associated with SSIs in patients undergoing cardiothoracic surgery. A comprehensive literature search adhering to Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines and based on the PICO paradigm was conducted across four databases: PubMed, Embase, Web of Science and the Cochrane Library, without any temporal restrictions. The meta‐analysis incorporated studies detailing the risk factors for post‐operative sternal infections, especially those reporting odds ratios (OR) or relative risks with 95% confidence intervals (CI). Quality assessment of the studies was done using the Newcastle‐Ottawa Scale. Statistical analysis was executed using the chi‐square tests for inter‐study heterogeneity, with further analyses depending on I2 values. Sensitivity analyses were performed, and potential publication bias was also assessed. An initial dataset of 2442 articles was refined to 21 articles after thorough evaluations based on inclusion and exclusion criteria. Patients with diabetes mellitus have an OR of 1.80 (95% CI: 1.40–2.20) for the incidence of SSIs, while obese patients demonstrate an OR of 1.63 (95% CI: 1.40–1.87). Individuals who undergo intraoperative blood transfusion present an OR of 1.13 (95% CI: 1.07–1.18), and smokers manifest an OR of 1.32 (95% CI: 1.03–1.60). These findings unequivocally indicate a pronounced association between these factors and an elevated risk of SSIs post‐operatively. This meta‐analysis confirms that diabetes, obesity, intraoperative transfusion and smoking heighten the risk of SSIs post‐cardiac surgery. Clinicians should be alert to these factors to optimise patient 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 | 0.028 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".