Robotic technology quantifies novel perceptual-motor impairments in patients with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Neurocognitive impairment is commonly reported in patients with chronic kidney disease (CKD). The precise nature of this impairment is unclear, due to the lack of objective and quantitative assessment tools used. The feasibility of using robotic technology to precisely quantify neurocognitive impairment in patients with CKD is unknown. METHODS: Patients with stage 4 and 5 CKD with no previous history of stroke or neurodegenerative disease were eligible for study enrollment. Feasibility was defined as successful study enrollment, high data capture rates (> 90%), and assessment tolerability. Our assessment included a traditional assessment: The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and a robot-based assessment: Kinarm. RESULTS: Our enrollment rate was 1.6 patients/month. All patients completed the RBANS portion of the assessment, with a 97.8% (range 92-100%) completion rate on Kinarm. Missing data on Kinarm were mainly due to time constraints. Data from 49 CKD patients were analyzed. Kinarm defined more individuals as impaired, compared to RBANS, particularly in the domains of perceptual-motor function (17-49% impairment), complex attention (22-49% impairment), and executive function (29-37.5% impairment). Demographic features (sex and education) predicted performance on some, but not all neurocognitive tasks. CONCLUSIONS: It is feasible to quantify neurocognitive impairments in patients with CKD using robotic technology. Kinarm characterized more patients with CKD as impaired, and importantly identified novel perceptual-motor impairments in these patients, when compared to traditional assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".