Turning and multitask gait unmask gait disturbance in mild‐to‐moderate multiple sclerosis: Underlying specific cortical thinning and connecting fibers damage
Bibliographic record
Abstract
Multiple sclerosis (MS) causes gait and cognitive impairments that are partially normalized by compensatory mechanisms. We aimed to identify the gait tasks that unmask gait disturbance and the underlying neural correlates in MS. We included 25 patients with MS (Expanded Disability Status Scale score: median 2.0, interquartile range 1.0-2.5) and 19 healthy controls. Fast-paced gait examinations with inertial measurement units were conducted, including straight or circular walking with or without cognitive/motor tasks, and the timed up and go test (TUG). Receiver operating characteristic curve analysis was performed to distinguish both groups by the gait parameters. The correlation between gait parameters and cortical thickness or fractional anisotropy values was examined by using three-dimensional T1-weighted imaging and diffusion tensor imaging, respectively (corrected p < .05). Total TUG duration (>6.0 s, sensitivity 88.0%, specificity 84.2%) and stride velocity during cognitive dual-task circular walking (<1.12 m/s, 84.0%, 84.2%) had the highest discriminative power of the two groups. Deterioration of these gait parameters was correlated with thinner cortical thickness in regional areas, including the left precuneus and left temporoparietal junction, overlapped with parts of the default mode network, ventral attention network, and frontoparietal network. Total TUG duration was negatively correlated with fractional anisotropy values in the deep cerebral white matter areas. Turning and multitask gait may be optimal to unveil partially compensated gait disturbance in patients with mild-to-moderate MS through dynamic balance control and multitask processing, based on the structural damage in functional networks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".