Comparing Three Dual-Task Methods and the Relationship to Physical and Cognitive Impairment in People with Multiple Sclerosis and Controls
Bibliographic record
Abstract
Dual-tasking (DT) is a measure to detect impairments in people with multiple sclerosis (MS). We compared three DT methods to determine whether cognitive (Montreal Cognitive Assessment (MoCA)) or physical disability (Expanded Disease Severity Scale; EDSS) was related to DT performance. We recruited MS participants with low disability (<3 EDSS, n = 13) and high disability (≥3 EDSS, n = 9) and matched controls (n = 13). Participants walked at self-selected (SS) speed on an instrumented walkway (Protokinetics, Havertown, USA), followed by DT walks in randomized order: DT ABC (reciting every second letter of the alphabet), DT 7 (serially subtracting 7's from 100), and DT 3 (counting upwards, leaving out multiples and numbers that include 3). DT 7 resulted in the most consistent changes in performance. Both MS and control groups reduced velocity and cadence and shortened step length during DT with no significant differences between groups. Control subjects widened stride width by about 1 cm while MS subjects (collapsed as one group) did not. MS subjects with higher disability significantly increased percentage time in double support during DT compared to SS (F = 12.95, p < 0.001). The change in DS was related to cognitive and not physical disability (r = 0.54, p < 0.05).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".