Correlation analysis between plasma biomarkers albumin, fibrinogen, and their ratio with postoperative delirium in patients undergoing non-cardiac surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: This meta-analysis aimed to investigate the correlation between plasma biomarkers, such as albumin and fibrinogen, and their ratio with postoperative delirium (POD) in patients undergoing non-cardiac surgery.Methods: Relevant observational cohort studies were systematically searched in PubMed, EMBASE, CINAHL, and the Cochrane Library databases as of March 2023.This meta-analysis was conducted using RevMan 5.4.1 and Stata 15.0 software.For continuous variables with non-uniform units, the standardized mean difference (SMD) and 95% confidence intervals (CIs) were used; otherwise, the mean difference (MD) and 95% CIs were employed.The Newcastle-Ottawa Scale (NOS) was applied to assess the quality of included literature.Results: Eighteen studies encompassing 7,011 patients were included.The meta-analysis revealed significantly lower albumin levels (sixteen studies, 5,813 patients, SMD = -0.45,95% CI = -0.64 to -0.26, P < 0.00001, I 2 = 80%) and albumin-fibrinogen ratio (AFR) (four studies, 824 patients, MD = -0.62,95% CI = -0.76 to -0.48, P = 0.56, I 2 = 0%) in the delirious group.Conversely, higher fibrinogen concentrations (two studies, 441 patients, MD = 0.13, 95% CI = 0.02 to 0.24, P = 0.69, I 2 = 0%) were observed in the delirious group.Due to high heterogeneity in albumin levels (P < 0.00001, I 2 = 80%), we conducted a subgroup and sensitivity analysis, and confirmed that the association of albumin levels was not influenced by surgery type, design or delirium evaluation instruments.Conclusions: Preoperative albumin, fibrinogen and AFR levels were associated with POD, potentially aiding in identifying high-risk patients and playing a key role in preventing POD.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".