Association Of Quality Of Sleep With Cognitive Decline Among The Patients Of Chronic Kidney Disease Undergoing Haemodialysis.
Bibliographic record
Abstract
BACKGROUND: This study was conducted to determine the association between the subjective quality of sleep and cognitive decline among the patients of chronic kidney disease (CKD) undergoing haemodialysis. METHODS: In this cross-sectional study 106 patients of chronic kidney disease (CKD) undergoing haemodialysis at a tertiary care hospital in Rawalpindi, Pakistan were included in the final analysis. Cognitive decline was measured by British Columbia Cognitive Complaints Inventory (BC-CCI). Sleep quality was measured by using the Pittsburgh Sleep Quality Index (PSQI). Relationship of age, gender, marital status, education, occupation, BMI, duration of dialysis, dialysis count per week, family income, tobacco smoking and use of naswar was assessed with the cognitive decline.. RESULTS: Out of 106 patients screened through BC-CCI and PSQI, 13.1% had no cognitive decline while 86.9% had significant cognitive decline. Relationship between quality of sleep and cognitive decline was significant on binary logistic regression.. CONCLUSIONS: This study showed significant relationship between the sleep quality and cognitive decline among the patients of CKD undergoing haemodialysis. The findings of our study also call for a greater degree of understanding of the physical and psychological state of patients of CKD undergoing haemodialysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".