Systematic review on efficacy of preventive measures for surgical site infection by multiple-drug resistant gram-negative bacilli
Bibliographic record
Abstract
BACKGROUND: There are no specific recommendations for prevention of surgical site infection (SSI) caused by multidrug resistant Gram-negative bacilli (MDR-GNB). Our objective was to systematically review the literature evaluating the efficacy and safety of measures specifically designed to prevent MDR-GNB SSI. METHODS: We searched MEDLINE, EMBASE, CINAHL and LILACS databases up to February 18, 2020. Randomized trials and observational cohort studies evaluating the efficacy of preventive measures against MDR-GNB SSI in adult surgical patients were eligible. We evaluated methodological quality of studies and general quality of evidence using Newcastle-Ottawa scale, Cochrane ROBINS-I and GRADE method. Random-effects meta-analyses were performed using Review Manager V.5.3 software. RESULTS: A total of 10,663 titles by searching databases were identified. Two retrospective observational studies, comparing surgical antibiotic prophylaxis (SAP) with or without aminoglycoside in renal transplantation recipients, and one non-randomized prospective study, evaluating ertapenem vs. cephalosporin plus metronidazole for SAP in extended spectrum beta-lactamase producing Enterobacteriales carriers undergoing colon surgery, were included. Risk of bias was high in all studies. Meta-analysis was performed for the renal transplantation studies, with 854 patients included. Combined relative risk (RR) for MDR GNB SSI was 0.57 (95%CI: 0.25-1.34), favoring SAP with aminoglycoside (GRADE: moderate). CONCLUSIONS: There are no sufficient data supporting specific measures against MDR-GNB SSI. Prospective, randomized studies are necessary to assess the efficacy and safety of SAP with aminoglycoside for MDR-GNB SSI prevention among renal transplantation recipients and other populations. PROSPERO 2018 CRD42018100845.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".