Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The instantaneous adaptations to speed and load changes during overground locomotion have major implications for mobility after stroke. We examined the extent to which stroke subjects could increase their overground walking speed with respect to speed and unloading changes. METHODS: Twelve subjects with a unilateral stroke were evaluated while walking overground full weight bearing (FWB) or with body weight support (BWS) at preferred or fast speed. On the basis of their preferred walking speed, subjects were classified as high (> or =45 cm/s) or low functioning (<45 cm/s). Gait speed, temporal distance factors (TDFs), as well as movements and muscle activation of the lower limbs were measured and compared across the conditions. RESULTS: FWB-Fast condition induced marked (165%) increment in gait speed in all subjects. BWS at preferred speed induced faster speeds in low- but not the high-functioning subjects, whereas combined BWS and fast walking yielded further speed increments in the high-functioning subjects. Fast walking was associated with bilateral increases in joint excursion and muscle activation, as well as improved symmetry in some TDFs. BWS favored a hip flexion strategy in early swing while decreasing limb circumduction. CONCLUSIONS: This study shows that stroke subjects can increase substantially their walking speed without deleterious effects. Fast walking induces marked speed-related improvements in body and limb kinematics and muscle activation patterns. BWS during overground walking also increases gait speed, but to a lesser extent and only in low-functioning subjects. The combination of BWS with fast speed produces the greatest increments in walking speed in all subjects.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.324 | 0.132 |
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".