The Prevalence of Frailty and its Association with Cognitive Dysfunction among Elderly Patients on Maintenance Hemodialysis: A Cross-Sectional Study from South India
Bibliographic record
Abstract
Data are scarce regarding the prevalence of frailty in elderly patients undergoing maintenance hemodialysis (HD) in India. We conducted a cross-sectional observational study aimed to study the prevalence of frailty and cognitive dysfunction in patients aged 75 years or more undergoing maintenance HD in three tertiary care hospitals and associated stand-alone dialysis centers in North Kerala. Frailty was ascertained by two methods. In method 1 (physical performance measurement based), dichotomous scoring (0 or 1) of five domains, namely weight loss, exhaustion, low physical activity, weak grip, and slow walking, was done, and a score of 3/5 was used to define frailty. In method 2 (self-report measure based), scores on the Medical Outcomes Study Short-Form 36-item Questionnaire (SF-36) physical function domain were used instead of hand grip strength and walking speed, and a score of <75 was defined as meeting the criteria for weakness and slow walking. Cognitive function was documented using the Montreal Cognitive Assessment Instrument. A total of 899 patients were screened, of whom 44 were aged 75 years or more and 39 met the criteria for inclusion. The majority (n = 31, 79.5%) had ages between 75 and 80 years and were male. Dialysis vintage was <1 year in 15.4%, 1-3 years in 51.3%, and >3 years in 33.3% of patients. Frailty was documented in 22 (56.4%) patients by method 1 and in 25 (64.1%) by method 2. There was a statistically significant difference between the two methods in documenting frailty (P < 0.001, Chi-square test). Cognitive impairment was present in 89.7% of patients and significantly associated with frailty (P < 0.001, Fisher's exact test). Frailty and cognitive dysfunction are highly prevalent in elderly people undergoing maintenance HD in North Kerala. Physical performance and self-report measure-based methods correlate well in frailty documentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".