A novel in-home digital treatment to improve processing speed in people with multiple sclerosis: A pilot study
Bibliographic record
Abstract
Objective: To assess whether a videogame-like digital treatment is superior to a control in improving processing speed in adults with multiple sclerosis (MS). Methods: Adults with MS and baseline Symbol Digit Modalities Test (SDMT) z-scores between −2 and 0 were enrolled in a double-blind randomized controlled clinical trial. After completing a baseline in-clinic evaluation (Visit 1), they were randomized to complete an in-home, tablet-based videogame-like digital treatment (AKL-T03) or control word game (AKL-T09) for up to 25 minutes/day, 5 days/week, for 6 weeks. A repeat in-clinic evaluation occurred at 6 weeks (Visit 2), and again 8 weeks later to determine persistence of effects (Visit 3). The pre-specified primary outcome was change in SDMT score between Visits 1 and 2. Results: SDMT increased at Visit 2 for participants randomized to both AKL-T03 ( p < 0.001) and AKL-T09 ( p = 0.024). These respective mean improvements were +6.10 and +3.55 (comparison p = 0.21). At Visit 3, 70% of participants randomized to AKL-T03 maintained a clinically meaningful 4+-point increase in SDMT above their baseline, compared with 37% for AKL-T09 ( p = 0.038). Conclusion: This in-home digital intervention resulted in substantial and durable improvements in processing speed. A larger randomized controlled clinical trial is planned. Trial Registration: This trial is registered on ClinicalTrials.gov under “NCT03569618,” https://clinicaltrials.gov/ct2/show/NCT03569618 .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".