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 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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".