A Feasibility Study of Working Memory Training for Individuals With Pediatric-Onset Multiple Sclerosis
Bibliographic record
Abstract
Cognitive impairment occurs in up to half of children and adolescents with multiple sclerosis (MS), and may be severe enough to compromise intellectual functioning, academic performance, and daily life function. Working memory (WM), which refers to the cognitive system that temporarily stores information long enough to use while manipulating the information for some purpose, is one of the major executive functions found to be compromised in pediatric-onset MS patients. The current dissertation sought to introduce a computerized cognitive training program (Cogmed) that is novel to the MS population in order to investigate feasibility, subjective experiences, and individual characteristics related to training outcomes, as well as examine preliminary efficacy of Cogmed in pediatric-onset MS patients. This dissertation employed mixed methods comprising Cogmed-specific training outcomes, performance on pre- and post-training neuropsychological assessment measures, and patient exit interviews. Pediatric-onset MS individuals who were identified as having cognitive difficulties (n = 9) underwent 5-6 weeks of intensive, home-based computerized training on verbal and visual-spatial WM exercises. Patients demonstrated general adherence and tolerance to Cogmed training, and completed training within the recommended 5-6 week timeframe. Almost all patients acknowledged changes in their WM performance as a result of training (n =8), and all patients (n = 9) described the training program as not intruding on their social lives. Age, disease onset, disease duration, and degree of brain atrophy emerged as potential predictors of individual training outcomes, as did intrinsic motivation. All individuals demonstrated improved performance on trained measures of WM and three individuals demonstrated improved performance on select non-trained measures of WM. The findings of this study demonstrate feasibility of implementing Cogmed in pediatric-onset MS patients, warranting subsequent large-scale randomized controlled studies that employ a multimodal approach to data analysis and that pay attention to individual differences that may predict variable training outcomes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".