Visuospatial and Executive Dysfunction in Patients With Acute Kidney Injury, Chronic Kidney Disease, and Kidney Failure: A Multilevel Modeling Analysis
Bibliographic record
Abstract
Background: Neurocognitive impairment is a common finding across the spectrum of kidney disease and carries important consequences for quality of life. We previously demonstrated that robotic technology can identify neurocognitive impairments not readily detectable by traditional testing in patients with acute kidney injury (AKI) and chronic kidney disease (CKD). Objective: The present study aimed to assess whether these quantifiable deficits in neurocognition differ based on a diagnosis of AKI, CKD, or kidney failure. Design: This was a cross-sectional analysis of participants previously enrolled in an observational study. Setting: Patients were enrolled at a tertiary academic hospital, Kingston Health Sciences Centre, Kingston, ON, Canada. Patients: Adults with AKI, CKD, or kidney failure. Measurements: Each participant underwent robotic neurocognitive assessment using the Kinarm: an interactive robotic device that uses a series of behavioral tasks involving movement of the upper limbs to precisely quantify neurocognitive impairment across a variety of neurocognitive domains. Methods: Multilevel modeling was used to determine the effect of Kinarm task type, kidney diagnostic group (AKI vs CKD vs kidney failure), and the interaction between the two, on neurocognitive performance. Results: = 0.50 [0.28, 0.72]), relative to all other tasks. There were no significant differences in average performance across tasks based on kidney diagnostic group. However, diagnostic group and neurocognitive task type interacted to determine performance, such that patients with AKI performed worse than those with either CKD or kidney failure on the Reverse Visually Guided Reaching task. Limitations: Kinarm assessment was performed at a single time point, and the sample size itself was small, which may lead to the risk of a false-positive association despite the use of multilevel modeling. Our sample size also did not permit inclusion of the underlying etiology of kidney impairment as a covariate in our analyses, which may have also influenced neurocognitive function. Conclusions: In this study that utilized the Kinarm to assess neurocognitive function, patients with AKI demonstrated significantly worse neurocognitive functioning than patients with CKD or kidney failure on a task measuring executive function and visuomotor control.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".