Examining cognitive speed and accuracy dysfunction in youth and young adults with pediatric-onset multiple sclerosis using a computerized neurocognitive battery.
Bibliographic record
Abstract
OBJECTIVE: We evaluated performance on the Penn Computerized Neurocognitive Battery (PCNB), a tool assessing accuracy and response time across four cognitive domains, alongside the Symbol Digit Modalities Test (SDMT), a measure of processing speed commonly used in MS. We determined whether performance decrements are more likely to be detected on measures of accuracy versus response time in pediatric-onset multiple sclerosis (POMS). METHODS: Performance on the SDMT, accuracy on PCNB tests belonging to four domains (executive function, episodic memory, complex cognition, social cognition), and response time on the PCNB were compared for 65 POMS patients (age range: 8-29 years) and 76 healthy controls (HCs) by ANCOVA. Associations between the Overall PCNB score and SDMT were examined for both groups, and their agreement in classifying impairment was assessed using Cohen's kappa. RESULTS: POMS patients (age at testing = 18.3 ± 4.0 years; age at POMS onset = 14.9 ± 2.3 years) demonstrated reduced accuracy relative to HCs on tests of working memory, attention/inhibition, verbal memory, and visuospatial processing, after adjusting for response time (p ≤ .002). Patients demonstrated slower overall response time on the PCNB (p = .003), while group differences on the SDMT did not meet significance (p = .03). Performance on the PCNB and SDMT were correlated (MS: r = 0.43, HC: r = 0.50, both p < .001), however, the degree of agreement for impairment was minimal (k = 0.22, p = .14). CONCLUSION: Specific cognitive deficits exist independently of slowed information processing speed in POMS, and may represent more significant areas of dysfunction. Delineation of accuracy and response time in neuropsychological assessment is important to identify areas of cognitive deficit in POMS. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".