Correlation of the Geriatric Nutritional Risk Index (<scp>GNRI</scp>) With Other Indicators of Nutrition in Chronic Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: The GNRI (Geriatric Nutritional Risk Index1) is an index used in geriatrics to predict the risk of complications and mortality associated with malnutrition. It considers serum albumin levels and the ratio of current weight or BMI to the ideal theoretical weight/BMI. AIM: The aim of this study was to evaluate this index in a population of metabolically stable chronic hemodialysis patients aged > 60 years and associate it with other nutritional markers. METHODS: The studied patient cohort was divided into two groups based on their Geriatric Nutritional Risk Index (GNRI) scores: Gr 1 with GNRI score < 97 and Gr 2 with GNRI ≥ 97. We registered the anthropometric, clinical, and biological data of the study population. RESULTS: One hundred seventy-seven patients (102 M-75F) undergoing chronic hemodialysis were included. There were no differences in age, muscle mass estimated by bioimpedance analysis, potassium levels, phosphorus levels, and nPCR between the groups. However, there were significant differences between the two groups concerning the primary disease. Gr 1 presented with a higher prevalence of diabetes and cardiovascular comorbidities. Additionally, Gr 1 presented with lower handgrip strength (Mean ± standard deviation in kg, 19.79 ± 9.37 vs. 26.83 ± 11.63, p = 0.05), lower fat mass index estimated by bioimpedance analysis (Mean ± standard deviation in kg/m2, 7.31 ± 4.55 vs. 15.24 ± 6.47, p < 0.001), and higher CRP levels (Mean ± standard deviation in mg/l, 22.27 ± 23.49 vs. 8.13 ± 10.14, p < 0.001). CONCLUSION: In conclusion, the GNRI, an easy calculation tool for nutrition assessment, is associated with important nutritional status parameters in chronic hemodialysis patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".