Does Poststroke Lower-Limb Spasticity Influence the Recovery of Standing Balance Control? A 2-Year Multilevel Growth Model
Bibliographic record
Abstract
Background Poststroke lower-limb spasticity (LLS) has been shown to degrade standing balance control by disrupting the temporal synchronization between individual limb centers of pressure (COPs). Time-varying changes in standing balance control associated with alterations in the extent of LLS have yet to be documented and are important to informing treatment strategies to improve such functional outcomes. Objective The present work aimed to understand the natural recovery of standing balance control among stroke survivors with LLS using limb-specific indices of standing balance control. Furthermore, we sought to understand if time-varying changes in LLS were associated with alterations in standing balance control. Methods A retrospective analysis of 92 participants was performed; 47 participants never exhibited LLS during the study (No_LLS), and 45 participants exhibited LLS during at least 1 testing session (LLS). Quiet standing for a duration of 30 s on 2 force platforms was recorded. Temporal synchrony and spatial symmetry of COP displacements were assessed, along with interlimb weight-bearing symmetry. Results All variables, except spatial symmetry, indicated initial improvement followed by deceleration in the rate of balance control recovery. Limb-specific measures indicated that individuals with LLS exhibited deficits in balance control. The recovery trajectories were not different between groups, suggesting a similar rate, but reduced extent, of balance control recovery among the LLS relative to the No_LLS group. Only temporal synchrony was altered by time-varying changes in spasticity. Conclusions The present results suggest that the reduction in spasticity may be beneficial to balance control recovery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".