Age of Onset as a Moderator of Cognitive Decline in Pediatric-Onset Multiple Sclerosis
Bibliographic record
Abstract
Cognitive impairment is often reported in pediatric-onset multiple sclerosis (MS). Using serial cognitive data from 35 individuals with pediatric-onset MS, this study examined how age at disease-onset and proxies of cognitive reserve may impact cognitive maturation over the course of childhood and adolescence. Neuropsychological evaluations were conducted at baseline and up to four more assessments. Of the 35 participants, 7 completed only one assessment, 5 completed two assessments, 13 completed three assessments, 10 completed four or more assessments. Growth curve modeling was used to assess longitudinal trajectories on the Trail Making Test-Part B (TMT-B) and the Symbol Digit Modalities (SDMT; oral version) and to examine how age at disease onset, baseline Full Scale IQ, and social status may moderate rate of change on these measures. Mean number of evaluations completed per patient was 2.8. Younger age at disease onset was associated with a greater likelihood of cognitive decline on both the TMT-B (p=.001) and SDMT (p=.005). Baseline IQ and parental social status did not moderate any of the cognitive trajectories. Findings suggest that younger age at disease-onset increases the vulnerability for disrupted performance on measures of information processing, visual scanning, perceptual/motor speed, and working memory. Proxies of cognitive reserve did not protect against the progression of decline on these measures. Young patients with MS should be advised to seek follow-up cognitive evaluation to assess cognitive maturation and to screen for the potential late emergence of cognitive deficits. (JINS, 2014, 20, 1-9).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".