A randomised controlled pilot trial protocol for patient led cognitive gamified training during haemodialysis
Bibliographic record
Abstract
Disruptions in cognitive function have been reported in individuals undergoing haemodialysis and those with chronic kidney disease. This pilot study protocol primarily assesses the feasibility and acceptability of using mobile cognitive gaming apps for patient-led cognitive training during haemodialysis sessions. The protocol consists of three phases: (1) reviewing and evaluating available cognitive gaming apps, (2) conducting focus groups/interviews with people with kidney disease to determine app preferences, and (3) undertaking a quasi-experimental randomised controlled trial to compare cognitive outcomes between a patient-led app intervention group and a standard care control group over four months. Primary outcomes will include changes in cognitive test scores [Montreal Cognitive Assessment (MoCA), Modified Mini-Mental State Exam (3MSE), Rapid Objective Working Memory Assessment (ROWMA)], while secondary outcomes will encompass quality of life measures [Patient-Reported Outcomes Measurement (PROM) Kidney Disease Quality of Life Short Form (KDQoL-SF™) v 1.3, Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health Instrument, European Quality of Life Five Dimension (EQ-5D)]. If demonstrated to be effective, this novel method of utilising gamified cognitive training applications could potentially mitigate cognitive decline and improve the well-being of people receiving haemodialysis without necessitating significant clinical resources. The findings from this research will guide the development of a larger definitive randomised trial in the future.
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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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.011 |
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".