Cognitive-motor dual task to reveal gait impairments in multiple sclerosis patients at an early stage: A systematic review
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis can cause locomotor and cognitive impairments even at lower levels of disability, which can impact daily life. The cognitive-motor dual task is commonly used to assess everyday locomotion. Thus, this study aimed to examine the effect of cognitive-motor dual tasks on gait parameters among patients with multiple sclerosis in the early disease stages and to determine whether dual tasks could be used as a clinical test to detect locomotion impairments. METHODS: A systematic search of five databases was conducted in May 2024. The population of interest was patients with multiple sclerosis with an Expanded Disability Status Scale score of 4 or less. The following outcome measures were examined: spatiotemporal and kinematic parameters. The Newcastle-Ottawa Scale was used to assess the quality of the studies. FINDINGS: Eleven studies including 270 patients with multiple sclerosis and 221 healthy controls. Three spatiotemporal parameters were modified both in patients with multiple sclerosis and healthy controls during dual-task performance: gait speed, stride length and the double support phase. No spatiotemporal parameter was affected during dual-task performance in patients with multiple sclerosis alone. INTERPRETATION: Dual-task performance could be useful for assessing gait impairments in patients with multiple sclerosis provided that assessments and protocols are standardized. Nevertheless, the spatiotemporal parameters did not allow discrimination between patients with multiple sclerosis at an early stage and healthy controls. Three-dimensional gait analysis during dual-task performance could be a useful approach for detecting early gait impairments in patients with multiple sclerosis, assessing their progression and adjusting rehabilitation programs.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".