Risk factors for surgical site infection in patients undergoing obstetrics and gynecology surgeries: A meta-analysis of observational studies
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify the risk factors for surgical site infection (SSI) in patients undergoing obstetrics and gynecology surgeries through meta-analysis. METHODS: Relevant original studies published from January 1945 to May 2023 were searched the CBM, PubMed, Embase, WOS, CNKI, Wanfang, vip, and Cochrane Library databases. Studies eligible were evaluated by two investigators following Newcastle-Ottawa Scale(NOS) criteria. Review Manager 5.3 software was used to analyse the combined effect sizes and test for heterogeneity, and Stata 14.0 software's Begg's Test and Egger's Test were used to test for bias. RESULTS: 13 case-control articles, including 860 cases in the case group and 13574 cases in the control group, met the inclusion criteria. Eventually, Our meta-analysis showed that SSI in patients undergoing obstetrics and gynecology surgeries was correlated with body mass index (BMI)≥24 (OR = 2.66; P < 0.0001), malignant lesions (OR = 4.65; P < 0.0001), operating time≥60min (OR = 2.58; P < 0.0001), intraoperative bleeding≥300ml (OR = 2.54; P < 0.0001), retained urinary catheter (OR = 4.45; P < 0.0001), and vaginal digital examination≥3times (OR = 2.52; P < 0.0001). CONCLUSION: In this study, BMI≥24, intraoperative bleeding≥300ml, malignant lesions, operating time≥60min, retained urinary catheter, and vaginal digital examination≥3times were considered as independent risk factors for SSI in obstetrics and gynecology surgery. It is recommended that scholars be rigorous in designing the experimental process when conducting case-control or experimental studies in order to improve the quality of the study. Controlling patients' weight before obstetrical and gynecological surgery, shortening the operation time intraoperatively, and strictly controlling the indications of vaginal digital examination and retained urinary catheter can effectively reduce the incidence of SSI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".