Bipedal hopping as a new measure to detect subtle sensorimotor impairment in people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Bipedal hopping has the potential to detect subtle multiple sclerosis (MS)-related impairments, especially among patients who "pass" typical movement tests. In this narrative review, we outline the biomechanics of bipedal hopping and propose its usefulness as a novel outcome measure for people with MS having mild disability. METHODS: We summarize articles that (1) examined the biomechanics of jumping or hopping and (2) tested the validity and/or reliability of hopping tests. We consolidated consistencies and gaps in research and opportunities for future development of the bipedal hop test. RESULTS: Bipedal hopping requires immense power, coordination, balance, and ability to reduce co-contraction; movement components typically affected by MS. These impairments can be measured and differentiated by examining specific variables, such as hop length (power), symmetry (coordination), center of pressure (balance), and coefficient of variability (co-contraction/spasticity). Bipedal hopping challenges these aspects of movement and exposes sensorimotor impairments that may not have been apparent during walking. CONCLUSIONS: Testing of bipedal hopping on an instrumented walkway may detect and monitor sensorimotor control in people with MS who do not currently present with clinical deficits. Early measurement is imperative for precise rehabilitation prescription to slow disability progression prior to onset of measurable gait impairment.Implications for rehabilitationJumping and hopping tests detect lower limb and balance impairments in children, athletes, and older adults.Bipedal hop test measures multiple domains: power, coordination, balance, and muscle timing.Bipedal hop test may expose subtle sensorimotor impairments in people with multiple sclerosis.Multiple variables measured can discern type of sensorimotor impairment to direct personalized 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".