Association between nutritional status and mortality/neurological outcomes in stroke patients: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically evaluate the association between Nutritional Risk Screening 2002 (NRS-2002), Controlling Nutritional Status (CONUT) score, Geriatric Nutritional Risk Index (GNRI), and 3-month mortality and poor neurological outcomes (modified Rankin Scale [mRS] ≥3) in stroke patients. METHODS: A systematic search of PubMed, Embase, and other databases up to April 2025 identified 11 observational studies (6 prospective cohorts; n = 7696). Study quality was assessed using the Newcastle-Ottawa Scale. Random-effects models were used to calculate pooled odds ratios (ORs) with 95 % confidence intervals (CIs). Heterogeneity, sensitivity, and publication bias were assessed. Meta-regression explored sources of heterogeneity. A gradient boosting classifier and Bayesian MCMC simulations were used for supplementary modeling. RESULTS: NRS-2002 ≥ 3 (OR=3.42, 95 % CI: 2.59-4.51), CONUT ≥5 (OR=3.66, 95 % CI: 2.47-5.43), and GNRI <98 (OR=2.68, 95 % CI: 1.86-3.84) were significantly associated with poor functional outcomes. These indices also predicted higher 3-month mortality: NRS-2002 ≥ 3 (OR=4.13), CONUT ≥5 (OR=3.57), GNRI <98 (OR=2.93). Heterogeneity ranged from moderate to high (I²=42.1-68.9 %). Meta-regression implicated regional and clinical factors as sources of variability. Predictive modeling (AUROC = 0.81) identified GNRI <92, age ≥75, and NIHSS as key mortality predictors, consistent with SHAP and Bayesian analyses. CONCLUSION: Malnutrition-particularly as defined by NRS-2002 ≥ 3, CONUT ≥5, and GNRI <98-is strongly linked to early mortality and poor recovery after stroke. GNRI showed high predictive value in older patients. Integrating nutritional screening into acute stroke care may enable early, cost-effective interventions to improve outcomes.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.007 | 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".