Data_Sheet_2_Factors contributing to sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis.CSV
Bibliographic record
Abstract
Background This study aims to systematically assess the risk factors, the overall strength of association, and evidence quality related to sepsis-associated encephalopathy. Methods A systematic search was conducted in the Cochrane Library, PubMed, Web of Science, and Embase for cohort or case-control studies published up to August 2023 on risk factors associated with sepsis-related encephalopathy. The selected studies were screened, data were extracted, and the quality was evaluated using the Newcastle–Ottawa Scale. Meta-analysis was performed using RevMan 5.3 software. The certainty of the evidence was assessed using the GRADE criteria. Results A total of 13 studies involving 1,906 participants were included in the analysis. Among these studies, 12 were of high quality, and one was of moderate quality. Our meta-analysis identified six risk factors significantly associated with Serious Adverse Events (SAE). These included APACHE II, SOFA, age, tau protein, and IL-6, which were found to be risk factors with significant effects (standard mean difference SMD: 1.24–2.30), and albumin, which was a risk factor with moderate effects (SMD: −0.55). However, the certainty of evidence for the risk factors identified in this meta-analysis ranged from low to medium. Conclusion This systematic review and meta-analysis identified several risk factors with moderate to significant effects. APACHE II, SOFA, age, tau protein, IL-6, and albumin were associated with sepsis-related encephalopathy and were supported by medium- to high-quality evidence. These findings provide healthcare professionals with an evidence-based foundation for managing and treating hospitalized adult patients with sepsis-related encephalopathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".