The role of serum albumin and albumin-related nutritional indices in predicting post-stroke cognitive impairment: a systematic review and meta-analysis
Bibliographic record
Abstract
Background The role of serum albumin levels and albumin-related nutritional indices in the prediction of cognitive impairment after stroke has not reached a uniform conclusion. Methods This study was prospectively registered in PROSPERO (CRD420251012150) and followed the PRISMA guidelines. We systematically searched six databases with a time frame from the date of database establishment to March 29, 2025. Literature selection and data collection were conducted by two researchers. Assessment of literature quality was performed according to the Newcastle-Ottawa Scale (NOS). Weighted mean difference (WMD) with 95% confidence intervals (CIs) was used to express pooled effect sizes. The chi-square (χ2) test (Cochran’s Q) and index of inconsistency ( I 2 ) were used to detect heterogeneity. Results A total of 9 studies involving 2,332 stroke patients were included in this meta-analysis. The results of this study showed that serum albumin levels (WMD: −3.85; 95% CI: −5.61, −2.09; p < 0.0001), Geriatric nutritional risk index (GNRI) (WMD: −2.68; 95% CI: −4.97, −0.39; p = 0.02), and HALP (hemoglobin, albumin, lymphocyte, and platelet) scores (WMD: −10.74; 95% CI: −19.98, −1.50; p = 0.02)were significantly lower in the post-stroke cognitive impairment (PSCI) compared to the post-stroke non-cognitive impairment (PSNCI). Conclusion Decreased serum albumin levels and albumin-related nutritional indices (GNRI and HALP scores) have a strong correlation with PSCI, which may become important indicators for early prediction of the development of PSCI. Systematic review registration https://www.crd.york.ac.uk/prospero/#recordDetails , identifier, CRD420251012150.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.034 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".