Angiopoietin-2 as a prognostic biomarker in septic adult patients: a systemic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The impairment of endothelial function represents a key pathophysiological mechanism in the development of sepsis. This research aimed to evaluate the prognostic significance of angiopoietin-2 (Ang-2), an endothelial biomarker, in predicting mortality in sepsis patients. METHODS: Chinese and English studies were systematically retrieved in PubMed, Cochrane Library, Embase, WanFang, CNKI, CQVIP, and CBM databases up to July 16, 2023. We conducted a study selection established upon predefined inclusion and exclusion criteria and used the Newcastle-Ottawa scale (NOS) to assess its quality. We extracted available data from the included studies for data analysis. RESULTS: The final inclusion comprised 33 studies with 4703 participants. According to the NOS, one study was of medium quality, while the rest were of high quality. In comparison to survivors, the levels of Ang-2 in non-survivors were markedly elevated [standardized mean difference (SMD) = 1.08, 95% confidence intervals (CI) 0.68-1.49, P < 0.001], and the same results were also observed in the subgroup that met sepsis 3.0 diagnosis criteria (SMD = 0.63, 95% CI 0.11-1.14, P = 0.017). The results comparing Ang-2 levels between non-survivors and survivors were independent of duration of follow-up, sample sources, type of study, and region. Ang-2 was a risk factor for mortality [odds ratios (OR) = 1.16, 95% CI 1.09-1.23, P < 0.001]. Ang-2 was demonstrated to be able to predict mortality in septic adult patients [area under the curve (AUC) = 0.76, 95% CI 0.70-0.82, P < 0.001]. CONCLUSIONS: Ang-2 level was positively correlated with risk of death in sepsis patients. Ang-2 might be a useful and valuable biomarker for predicting mortality in septic adult patients.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.007 | 0.009 |
| 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".