A meta-analysis of risk factors for non-superficial surgical site infection following spinal surgery
Bibliographic record
Abstract
Abstract Background Surgical site infection (SSI) is the most common complications in spinal surgery. In SSI, non-superficial surgical site infections are more likely to result in poor clinical outcomes. It has been reported that there are multiple factors contributing to postoperative non-superficial SSI, but still remains controversial. Therefore, the aim of this meta-analysis is to investigate the potential risk factors for non-superficial SSI following spinal surgery. Methods A systematic database search of PubMed, Embase, Web of Science, Cochrane Library and Clinical Trials was performed for relevant articles published until September 2022. According to the inclusion and exclusion criteria, two evaluators independently conducted literature screening, data extraction and quality evaluation of the obtained literature. The Newcastle–Ottawa Scale (NOS) score was used for quality evaluation, and meta-analysis was performed by STATA 14.0 software. Results A total of 3660 relevant articles were initially identified and 11 articles were finally included in this study for data extraction and meta-analysis. The results of meta-analysis showed that the diabetes mellitus, obesity, using steroids, drainage time and operative time were related to the non-superficial SSI. The OR values (95%CI) of these five factors were 1.527 (1.196, 1.949); 1.314 (1.128, 1.532); 1.687(1.317, 2.162); 1.531(1.313, 1.786) and 4.255(2.612, 6.932) respectively. Conclusions Diabetes mellitus, obesity, using steroids, drainage time and operative time are the current risk factors for non-superficial SSI following spinal surgery. In this study, operative time is the most important risk factor resulting in postoperative 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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.069 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".