Prognostic role of elevated VEGF in sepsis: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: The incidence and mortality of sepsis are increasing year by year, and there is still a lack of specific biomarkers to predict its prognosis. Prognostic value of vascular endothelial growth factor (VEGF) in predicting the severity and mortality of sepsis has been gradually discovered. Methods: Literature was searched through Embase, PubMed, Web of Science, China National Knowledge Infrastructure(CNKI) and Cochrane Library databases in March 2022. Observational studies, evaluating the impact of VEGF in sepsis outcomes (mortality and severity) are included in this meta-analysis. Risk of bias was assessed with the Newcastle-Ottawa Scale (NOS). Sensitivity and publication bias analyses were also assessed. Meta-regression analysis were performed to identify the potential sources of heterogeneity. Result: A total of 1,574 articles were retrieved from the systematic literature search. We included 20 studies for qualitative and quantitative analysis. Deceased and critically ill patients had higher baseline VEGF levels than survivors and non-severe patients. The pooled sensitivity and specificity for VEGF predicts sepsis mortality were 0.79and 0.76, respectively. the area under the SROC curve was 0.83. Conclusion: High VEGF are associated with poor clinical outcomes for patients diagnosed with sepsis. This study was recorded on PROSPERO, under the registration ID: CRD42022323079.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".