Association between Geriatric Nutritional Risk Index and survival outcomes in patients with urological cancers: an updated meta-analysis
Bibliographic record
Abstract
OBJECTIVES: This meta-analysis aimed to evaluate the association between the Geriatric Nutritional Risk Index (GNRI) and survival outcomes in patients with urological cancer. DESIGN: Systematic review and meta-analysis of observational studies. DATA SOURCES: A comprehensive literature search was conducted in Medline, EMBASE, Google Scholar and the Cochrane Library from inception to 7 July 2024. ELIGIBILITY CRITERIA: Studies were included if they examined the correlation between the GNRI and long-term survival outcomes in adult patients (≥18 years old) with urological cancers. DATA EXTRACTION AND SYNTHESIS: Two researchers independently extracted data and assessed study quality using the Newcastle-Ottawa Scale and certainty of evidence using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) methodology. Publication bias was evaluated using funnel plots and Egger's test for outcomes with more than 10 studies. Pooled HRs and 95% CIs were calculated using a random-effects model. Subgroup analyses, meta-regression and sensitivity analyses were performed. RESULTS: 17 studies involving 8816 patients were included. Study quality assessment showed that 15 studies had a low risk of bias (scores 7-9) and two had a high risk (scores 5-6). Low GNRI was significantly associated with poor overall survival (OS) (HR: 2.6, 95% CI: 2.0 to 3.38, p<0.00001, I² = 64%, 13 studies), cancer-specific survival (CSS) (HR: 2.65, 95% CI: 1.76 to 3.98, p<0.00001, I² = 75%, 7 studies), recurrence-free survival (RFS) (HR: 1.47, 95% CI: 1.02 to 2.1, p=0.04, I² = 58%, four studies) and progression-free survival (PFS) (HR: 1.86, 95% CI: 1.54 to 2.23, p<0.00001, I² = 0%, five studies). Funnel plot and Egger's test (p=0.948) indicated a low risk of publication bias for OS. GRADE assessment showed low certainty of evidence for OS and PFS, and very low certainty for CSS and RFS. Meta-regression identified follow-up time and sample size as significant sources of heterogeneity. CONCLUSIONS: A low GNRI is significantly associated with poor survival outcomes in patients with urological cancer. The GNRI may serve as a valuable prognostic tool in clinical practice. Further research is needed to validate these findings in diverse populations and to explore the underlying biological mechanisms. PROSPERO REGISTRATION NUMBER: CRD42023476678.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.064 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".