Relationship between <scp>G</scp>eriatric <scp>N</scp>utritional <scp>R</scp>isk <scp>I</scp>ndex and total lymphocyte count and mortality of hemodialysis patients
Bibliographic record
Abstract
We examined the relationships between Geriatric Nutritional Risk Index (GNRI), total lymphocyte count (TLC), and mortality in hemodialysis (HD) patients. We examined GNRI and TLC in 120 maintenance HD patients and followed these patients for 120 months. Predictors of all-cause death were examined using life table analysis and the Cox proportional hazards model. TLC marginally correlated with GNRI (r = 0.176; p = 0.090) and significantly with phosphorus levels (r = 0.206; p = 0.026). Life table analysis revealed that subjects with a GNRI < 90 (n = 19) had lower survival rates than did those with a GNRI ≥ 90 (n = 101; Wilcoxon's test, p = 0.048), but subjects with a TLC < 1500/mm(3) (n = 76) had similar survival rates compared with subjects with a TLC ≥ 1500/mm(3) (n = 44; Wilcoxon's test, p = 0.500). Multivariate Cox proportional hazards analyses demonstrated that GNRI is a significant predictor of mortality (hazard ratio 9.315, 95% confidence interval 1.161-74.753, p = 0.036), after adjusting for age, sex, presence of type 2 diabetes mellitus, Kt/V, normalized protein catabolic rate, hematocrit, phosphorus, systolic blood pressure and TLC. Our findings suggest the TLC may be used as a simple nutritional tool, but may not be a predictor of mortality in HD patients. These findings require confirmation by further studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".