SP422BODY COMPOSITION IS ASSOCIATED WITH QUALITY OF LIFE AND COGNITIVE STATUS IN PATIENTS WITH CHRONIC RENAL FAILURE
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Recent studies demonstrated that the body composition, hydration status and muscular mass, are correlated with poor outcomes and quality of life in elderly. Dehydration is also a strong risk factor for dementia. Patients with chronic renal failure from early-stage disease have a reduced quality of life and cognitive impairment that gets worse with the advanced disease.Aim of our study was to investigate the correlation among the body composition, the quality of life and the cognitive status in patients with chronic renal failure. METHODS: We enrolled 15 patients with stage 3-4 KDOQI chronic renal failure. Patients underwent bioimpedentiometry (BIA) and the following tests: Kidney Disease Quality of Life Short Form (KDQOL-SF 1.3) to assess the quality of life, Mini-Mental State Exam (MMSE) and Montreal Cognitive Assessment (MoCA) to assess the cognitive status. We correlated the BIA data with the results of the tests by linear correlation. Data are presented as mean and standard deviation. SP422 Figure 1 SP422 Figure 2 CONCLUSIONS: Our results suggest that the body composition and the water distribution into body and lean max, could influence the quality of life and the cognitive function in chronic renal failure patients. Therefore, a regular monitoring of the body composition by BIA, the cognitive status by MMSE and MoCA test and the quality of life by KDQOL-SF could be desirable.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".