Incidence and risk factors for surgical site infection after open reduction and internal fixation of tibial plateau fracture: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to quantitatively summarize the risk factors associated with surgical site infection after open reduction and internal fixation of tibial plateau fracture. METHODS: Medline, Embase, CNKI, Wanfang database and Cochrane central database were searched for relevant original studies from database inception to October 2016. Eligible studies had to meet quality assessment criteria according to the Newcastle-Ottawa Scale, and had to evaluate the risk factors for surgical site infection after open reduction and internal fixation of tibial plateau fracture. Stata 11.0 software was used for this meta-analysis. RESULTS: Eight studies involving 2214 cases of tibial plateau fracture treated by open reduction and internal fixation and 219 cases of surgical site infection were included in this meta-analysis. The following parameters were identified as significant risk factors for surgical site infection after open reduction and internal fixation of tibial plateau fracture (p < 0.05): open fracture (OR 3.78; 95% CI 2.71-5.27), compartment syndrome (OR 3.53; 95% CI 2.13-5.86), operative time (OR 2.15; 95% CI 1.53-3.02), tobacco use (OR 2.13; 95% CI 1.13-3.99), and external fixation (OR 2.07; 95% CI 1.05-4.09). Other factors, including male sex, were not identified as risk factors for surgical site infection. CONCLUSION: Patients with the abovementioned medical conditions are at risk of surgical site infection after open reduction and internal fixation of tibial plateau fracture. Surgeons should be cognizant of these risks and give relevant preoperative advice.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.009 | 0.009 |
| 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".