SP589COGNITIVE FUNCTION TESTED BY MULTI-DOMAIN ASSESSMENT IS REDUCED DURING HAEMODIALYSIS
Bibliographic record
Abstract
Introduction and Aims: Patients with established renal failure (ERF) receiving dialysis score lower on cognitive functioning testing than the general population. Analogous to myocardial stunning observed during dialysis we performed assessments of cognitive function during and out with haemodialysis to determine the effect dialysis may have on cerebral function. Methods: Patients receiving hospital haemodialysis for ERF in a large tertiary referral centre were recruited. We excluded all those with known cerebrovascular or cognitive disorders. A neurocognitive battery was performed during a routine dialysis session and on a non-dialysis day, allowing a gap of 3-4 weeks, to reduce learning effect. We used a multi-domain assessment consisting of the Montreal Cognitive Assessment (MOCA), Semantic and Phonemic fluency tests, Letter Digit Substitution Test (LDST), Trail Making Test A and B (TMT-A, TMT-B) and the revised Hopkins Verbal Learning Test (HLVT-R). Baseline cognitive function was compared to the 50th percentile population normative values (matched for age, sex and educational level where appropriate) and a Wilcoxon-signed rank test applied to compare scores during and out with dialysis. Data were analysed using SPSS v22.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.001 | 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".