Acute kidney injury is associated with subtle but quantifiable neurocognitive impairments
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is associated with long-term morbidity and mortality. The effects of AKI on neurocognitive functioning remain unknown. Our objective was to quantify neurocognitive impairment after an episode of AKI. METHODS: Survivors of AKI were compared with age-matched controls, as well as a convenience sample of patients matched for cardiovascular risk factors with normal kidney function (active control group). Patients with AKI completed two assessments, while the active control group completed one assessment. The assessment included a standardized test: the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and a robotic assessment: Kinarm. RESULTS: The cohort consisted of 21 patients with AKI, 16 of whom completed both assessments, and 21 active control patients. The majority of patients with AKI had Kidney Disease: Improving Global Outcomes Stage 3 AKI (86%), 57% received dialysis and 43% recovered to ≤25% of their baseline serum creatinine by their first assessment. Compared with the RBANS, which detected little impairment, the Kinarm categorized patients as impaired in visuomotor (10/21, 48%), attention (10/20, 50%) and executive tasks (11/21, 52%) compared with healthy controls. Additionally, patients with AKI performed significantly worse in attention and visuomotor domains when compared with the active controls. Neurocognitive performance was generally not impacted by the need for dialysis or whether kidney function recovered. CONCLUSIONS: Robotic technology identified quantifiable neurocognitive impairment in survivors of AKI. Deficits were noted particularly in attention, visuomotor and executive domains. Further investigation into the downstream health consequences of these neurocognitive impairments is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".