Role of obesity in reducing the risk of mortality in sepsis: a meta-analysis of observational studies
Bibliographic record
Abstract
In this research, We evaluated obesity, obesity and mortality in patients with septicemia, increases the mortality risk in sepsis and obese patients. We used data sources, such as PubMed, Embase, and the Cochrane Library, without language restrictions. In this project, a prospective population study was used to compare septicemia, obesity group and healthy control group, and to analyze the disability rate and mortality rate of septicemia and obesity group. The risk of deviation was evaluated by Newcastle Ottawa quality Assessment scale (New Caussley Review Scale Scale, NOS). In data synthesis, data with odds ratio (OR) and 95% confidence interval (CI) are collected for Meta analysis, additionally, publication bias was investigated using a funnel plot. The pooled prevalence of mortality in patients with sepsis and obesity (this included 23,358 individuals) was 22.2% (95% CI: 14.2%-30.2%; I2=99.3%; P<0.001). Eight cohort studies investigated survival in hospitalized patients with obesity and sepsis, and pooled analysis showed decreased mortality (OR=0.73; 95% CI: 0.60-0.89; I2=71.9%; P<0.001). However, in ICU (ICU), the proportion of patients with septicemia and obesity was reduced. (OR=0.71; 95% CI: 0.56-0.91; I2=29.6%; P=0.006) but was not linked to a higher chance of dying in a hospital (OR=0.75; 95%CI: 0.55-1.03; I2=80.7%; P=0.077). At follow-up, there was no change in 30-day mortality, but there was a reduction in mortality at the 1-year follow-up. From this meta-analysis, it was found that there was a 27% reduction in mortality in patients with sepsis and obesity, which may confirm the obesity paradox. More prospective trials and mechanistic studies are required to validate this hypothesis.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.056 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.002 | 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".