Effect of Comorbidities on the Incidence of Surgical Site Infection in Patients Undergoing Emergency Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Surgical site infection (SSI) is a significant concern in patients undergoing emergency surgery, particularly in those with underlying comorbidities. This meta-analysis aimed to evaluate the effect of comorbidities, including diabetes mellitus, hypertension, obesity, pulmonary disease, cardiac disease, liver disease, and renal disease, on the incidence of SSI in patients undergoing emergency surgery. Methods: We performed a systematic literature search across electronic databases including PubMed, ScienceDirect, Cochrane Library, ProQuest, and Google Scholar to identify studies examining the effect of comorbidities on the incidence of SSI in patients undergoing emergency surgery. To determine the effect size, pooled odds ratios (ORs) were calculated. Statistical analysis was performed using Review Manager 5.3 software. Results: Thirteen studies involving 8,952 patients undergoing emergency surgery were included in this meta-analysis. The pooled analysis showed that the following comorbidities significantly increased the risk of SSI following emergency surgery: diabetes mellitus (OR = 2.22; 95% confidence interval (CI) = 1.52 - 3.25; P < 0.0001), obesity (OR = 1.43; 95% CI = 1.19 - 1.72; P = 0.0001), and liver disease (OR = 1.66; 95% CI = 1.37 - 2.00; P < 0.00001). However, hypertension, pulmonary disease, cardiac disease, and renal disease showed no significant association with SSI. Conclusions: In patients undergoing emergency surgery, the presence of comorbidities including diabetes mellitus, obesity, and liver disease increases the incidence of developing SSI.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.049 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".