A meta‐analysis of the risk factors of surgical site infection after hysterectomy for endometrial cancer
Bibliographic record
Abstract
Surgical Site Infection (SSI) is one of the common postoperative complications after hysterectomy for endometrial cancer (EC). Previous studies have investigated the risk factors for SSI in patients with EC. However, big differences in research results exist, and the correlation coefficients of different research results are quite different. A meta-analysis was conducted to examine the risk factors related to SSI in patients with EC. We searched English databases to collect case-control studies or cohort studies published before July 20, 2023, including PubMed, Web of Science, Embase and ScienceDirect. The risk of bias in the included studies was assessed via Newcastle-Ottawa Scale. The analysis was performed using RevMan 5.4.1 tool. A total of 6 articles (n = 3647) were selected in this meta-analysis. The following risk factors were presented to be significantly correlated with SSI in EC: laparotomy (OR = 2.66, 95% CI [1.57, 4.54]), postoperative blood sugar ≥10 mmol/L (OR = 4.38, 95% CI [2.83, 6.78]), Federation International of Gynaecology and Obstetrics (FIGO) stage-III or IV (OR = 2.27, 95% CI [1.49, 3.46]). The occurrence of SSI is influenced by a variety of factors. Thus, we should pay close attention to high-risk subjects and take crucial targeted interventions to lower the SSI risk after hysterectomy. Owing to the limited quality and quantity of the included studies, more rigorous studies with adequate sample sizes are needed to verify the conclusion.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".