Effects of Pre-hospital Metformin Use on Clinical Outcomes in Patients with Diabetes and Sepsis
Bibliographic record
Abstract
Background There is growing evidence that pre-hospital use of metformin reduces mortality in patients with diabetes and sepsis; however, the effect of metformin on clinical outcomes in patients with diabetes and sepsis remains controversial. Therefore, there is a need for a systematic review and Meta-analysis of existing studies to further evaluate whether metformin can improve mortality and other clinical outcomes in patients with diabetes and sepsis. Objective To systematically evaluate the effects of pre-hospital metformin use on mortality, initial plasma lactate level and organ dysfunction in patients with diabetes and sepsis. Methods PubMed, Embase, Cochrane Library, Web of Science, Scopus, China Biomedical Literature Service System, Wanfang Data, CNKI and other databases were searched by computer for Chinese and English literature on the effects of pre-hospital metformin use on clinical outcomes in patients with sepsis and diabetes from inception to March 15, 2023. The test group (MET group) involved adult patients with diabetes and sepsis received pre-hospital metformin use, and the control group (NM group) involved adult patients with diabetes and sepsis who did not receive pre-hospital metformin use. After screening, data extraction and literature quality evaluation were conducted by two researchers, Meta-analysis was performed using RevMan 5.3 software. Results A total of 12 papers were included in this study, involving 12 320 patients with diabetes and sepsis, with Newcastle-Ottawa Scale (NOS) scores of 7-8. The results of Meta-analysis showed that the mortality rate (OR=0.61, 95%CI=0.46 to 0.80, P<0.001) and the use rate of vasopressors (OR=0.83, 95%CI=0.69 to 0.98, P=0.03) in MET group were lower than those in NM group; there was no significant difference in initial plasma lactate level (MD=0.31, 95%CI=-0.12 to 0.75, P=0.16) , serum creatinine level (MD=-0.81, 95%CI=-0.48 to 0.13, P=0.25) , initial blood glucose level (MD=32.94, 95%CI=-10.12 to 76.01, P=0.13) and mechanical ventilation (OR=0.90, 95%CI=0.77 to 1.06, P=0.23) between the MET group and NM group. Conclusion The pre-hospital use of metformin can reduce the mortality of patients with diabetes and sepsis, and reduce the use rate of vasoppressors in corresponding patients, providing updated evidence that metformin can reduce the mortality of patients with diabetes and sepsis. However, whether it can reduce the sepsis severity score and reduce the maximum dose of norepinephrine in patients with diabetes and sepsis remains to be further studied.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".