Comparison of creatinine index and geriatric nutritional risk index for nutritional evaluation of patients with hemodialysis
Bibliographic record
Abstract
INTRODUCTION: Malnutrition is prevalent in hemodialysis (HD) patients, and the risk of mortality is strongly correlated with malnutrition. Current methods of nutritional evaluation are mostly subjective, time-consuming, and cumbersome. Creatinine index (CI) and geriatric nutritional risk index (GNRI) are very simple and objective methods to assess the nutritional status of HD patients. The present study compares the performance of CI and GNRI as nutritional risk assessment tools. METHODS: Eighty-eight patients with end-stage renal disease on HD were recruited from a single tertiary center. A clinical dietitian carried out individual interviews of all patients and made nutritional diagnosis. Demographic and clinical data were also used to derive GNRI and CI over 4 months. FINDINGS: Thirty-eight out of 88 patients (44%) were diagnosed with normal nutritional status. Twenty-two patients (25%) were diagnosed with severe malnutrition and 27 (31%) had moderate malnutrition. Compared with patients with severe malnutrition, the normal group and those with moderate malnutrition showed significantly higher levels of body mass index and GNRI. Even though GNRI was associated with CI, protein intake, uric acid, and normalized protein nitrogen were not significantly correlated with GNRI, whereas the markers were highly associated with CI (P = 0.000). GNRI enable the identification of the severe malnutrition group but not the normal and moderate-malnutrition groups. However, based on CI, the normal group was distinguished while those with severe and moderate malnutrition were not. DISCUSSION: Either CI or GNRI was a valid tool for longitudinal observation of nutritional status of patients on chronic HD and facilitated the screening of cases with malnutrition. Compared with GNRI, CI ranked higher in performance for the assessment and monitoring of nutritional status in HD 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".