SP702ROLE OF INFLAMMATION IN MALNUTRITION AND COGNITIVE IMPAIRMENT IN DIALYSIS PATIENTS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Chronic inflammation is a common issue among dialysis patients that is associated with decreased survival and quality of life (QoL). It is associated to several other comorbid conditions such as oxidative stress, endothelial dysfunction and vascular calcifications that are leading cause of both malnutrition and cognitive impairment in this population.Many studies evaluated the pathophysiologic link between inflammation and malnutrition, as well as cognitive impairment, but there are no evidence of an interconnection between all these factors.Therefore, aim of these study was to evaluate the association between cognitive and nutritional status, body composition and marker of inflammation in End Stage Renal Disease. METHODS: Dialysis patients without neurological disorders were selected. Bioimpedentiometry (BCM Fresenius), Malnutrition Inflammation Score (MIS), Montreal Cognitive Assessment (MOCA), Trail Making Test A-B (TMT) and biochemical tests were performed before dialysis session. Statistical analysis were conducted using t-test, ANOVA and multiple linear regression adjusted for age, sex, time on dialysis and education. RESULTS: 53 patients (35 Hemodialysis, 18 Peritoneal dialysis) were enrolled (age 60.1 ± 12.9 years). Mean MOCA score was 19.5 ± 5.1. Lean Tissue Mass and percentage of Fat were significantly higher in patients with poor executive functions at TMT (p=0.04 and 0.01 respectively) and linearly associated with cognitive functions assessed with MOCA (p=0.003 and 0.02 respectively). Both MOCA (p=0.003) and TMT (p=0.03) worsened significantly progressively with the malnutrition level measured by MIS (Figure 1). Moreover, TMT and MIS score resulted significantly associated inversely with serum Albumine and directly with CRP. SP702 Figure
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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 | 0.001 | 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".