Assessing the gait kinematics of people with multiple sclerosis
Bibliographic record
Abstract
The effects of multiple sclerosis (MS) on the central nervous system often manifest as abnormalities in gait kinematics. Clinically practical, valid, and reliable measures of gait kinematics are necessary to address research questions about these abnormalities. Consequently, this study examined the feasibility of measuring gait kinematics of people with MS (PWMS) using two such measures: wireless flexible electrogoniometry (EG) and the GAITRite walkway system. Six PWMS and six controls without MS walked at a self-selected comfortable speed. Angle at initial contact and total joint excursion at both the knee and ankle were measured by EG. Speed, cadence, step length, stride length, stance duration and double support duration were measured by GAITRite. PWMS showed significantly reduced speed, cadence, and ankle excursion and increased stance and double support duration as previously shown with 3D motion analysis. Spasticity and/or instability may lead to these kinematic gait abnormalities in PWMS. As well, reduced velocity may be a cause or effect of any of the other observed gait abnormalities. Further research about the determinants of gait dysfunction in PWMS is required. EG and GAITRite are clinically practical, valid and reliable measures of gait kinematics and should be included in further clinic-based research to determine which kinematic gait abnormalities are causes and which are effects of the observed decrease in gait speed in PWMS. Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council and Canada Research Chair Student Funds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".