Prevalence, types and recognition of cognitive impairment in dialysis patients in South Eastern Sydney
Bibliographic record
Abstract
BACKGROUND: In international studies, cognitive impairment is a common but underdetected issue in dialysis patients. Chronic kidney disease (CKD) shares risk factors with and is an independent risk factor for cognitive impairment. There is a lack of Australian data on cognitive impairment in this at-risk population. This has implications on service planning because cognitive impairment in CKD is associated with higher mortality, morbidity and healthcare costs. AIMS: To examine the prevalence, types and clinician recognition of cognitive impairment within an Australian dialysis population. METHODS: A cross-sectional study of haemodialysis and peritoneal dialysis patients in South Eastern Sydney screened for cognitive impairment using the Montreal Cognitive Assessment (MoCA). Participant interviews, medical records, physician and carer questionnaires, were used to determine the types of cognitive impairment and rate of recognition. RESULTS: One hundred and six participants were included (median age 66 years, median dialysis duration 2 years) and 58 (54.7%) were cognitively impaired on the MoCA, of whom old age psychiatrists sub-classified 21 (36.2%) as having dementia, and 31 (53.4%) with 'cognitive impairment, no dementia'; 36/58 (62.0%) of the cognitively impaired participants on the MoCA were suspected of having cognitive impairment by nephrologists but only 14/58 (24.1%) had this documented in medical records. CONCLUSION: Although cognitive impairment is common in dialysis patients, there are low levels of detection by clinical teams. Cognitive screening of dialysis patients should be incorporated as part of wider assessment and determination of management goals such as individuals' capacity to self-care and provide informed consent to treatments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".