Computerized Neuropsychological Testing to Rapidly Evaluate Cognition in Pediatric Patients With Neurologic Disorders
Bibliographic record
Abstract
Computerized neuropsychological tests represent a viable method for rapidly screening cognition. The purpose of this study was to explore performance on the CNS Vital Signs in a large pediatric neurology sample. Participants included 166 neurology patients (mean age, 13.0 years; standard deviation, 3.2) and 281 controls (mean age, 13.2 years; standard deviation, 3.2) between 7 and 19 years. The neurology sample performed significantly worse on all domain scores and nearly all subtest scores. Cohen d effect sizes were small to medium for verbal memory (d= 0.44), visual memory (d= 0.40), and reaction time (d= 0.48) and very large for psychomotor speed (d= 1.19), complex attention (d = 0.94), cognitive flexibility (d = 0.94), and the overall composite score (d = 1.08). Using the criterion for cognitive impairment of 2 or more scores ≤5th percentile, 36.6% of the neurology sample was identified as having an uncommon cognitive profile. This is the first study to demonstrate the performance of pediatric patients with neurologic disorders on CNS Vital Signs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".