P1501KIDNEY FAILURE AND BRAIN FUNCTION DECLINE
Bibliographic record
Abstract
Abstract Background and Aims: Chronic kidney disease (CKD) and cognitive impairment (CI) are two major health problems in an aging population, and both carry out negative prognostic implications. Prevalence in general population appears to be around 22.2% and recent analysis indicates that CI and frailty can be more prevalent in individuals undergoing hemodialysis (HD). Many causes can contribute to this higher prevalence, from vascular calcification to cerebral hypoperfusion, oxidative damage and uremic toxins. Both frailty and CI can lead to an increase morbimortality. Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE) are two screening instruments with a good application profile for cognitive evaluation, as well as the Frailty Clinical Scale (FCS). However, there are few studies using these scales on HD patients and demonstrating association between frailty, cognitive impairment and their clinical characteristics. The aim of this study is to investigate the prevalence of coexisting cognitive impairment and frailty in our center hemodialysis patients and its association with clinical characteristics and outcomes. Method: Thirty-two patients undergoing hospital hemodialysis program were assessed. The MoCA scale, MMSE and FCS were applied. Data were analyzed using appropriate statistical methods, using SPSS ® version 22.0. The significance level considered was 5%. Results: Thirty-two patients aged between 30 and 90 years were evaluated, with a mean of 61.63 years (SD ± 18.26), without gender predominance. The prevalence of deficits was 78.1% and 37,5% in MoCA and MMSE, respectively, without differences between gender. The prevalence of frailty (≥3) was 43.8%. Patients with deficit assessed by MoCA and MMSE were on average 15 years and 20 years older, respectively, than patients without deficit (p = 0.002). We found a statistically significant association between deficit measured by MMSE and frailty (p <.001), with higher prevalence of frailty (83.3%) in individuals with deficit compared to individuals without deficit, where the prevalence of frailty was 20.0%. The deficit assessed by MMSE was also associated with time on dialysis (p = .029). No statistically significant associations were detected between MoCA and frailty over time on dialysis or between deficit measured by MoCA and frailty. Regarding patients’ comorbidities, there were no statistically significant differences between deficit assessed by MoCA and MMSE and presence of diabetes mellitus, hypertension and dyslipidemia. Deficits assessed by MoCA, MMSE, and Frailty were not associated with phosphoremia and also there was no association between presence of significative hypotension episodes during HD and these scales. Dialysis efficacy (kt/v) was not statistically associated with MoCA and MMSE deficits. Similarly, no association was found between Kt / v and frailty. Conclusion: In our study, prevalence of CI and frailty in hemodialysis patients was high. Time on dialysis program was related in a statistically significant way with CI and there was higher prevalence of frailty in individuals with deficit measured by MMSE. However, we did not find a correlation between dialysis efficacy, comorbidities and vascular risk factors and cognitive deficit or frailty score. The epidemiology and natural history of cognitive impairment and its association with frailty are important to understand among patients on HD for early intervention and management.
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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 | 0.000 |
| 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) | 0.004 | 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".