Investigating the optimal reactive balance training intensity in people with chronic stroke: Study protocol for a randomized control trial
Bibliographic record
Abstract
Stroke significantly contributes to long-term disability, one of the problems is with impaired balance control, increasing the risk of falls. The risk of falls may be mitigated using reactive balance training (RBT) which has been shown to effectively reduce fall risk by enhancing reactive stepping following repeated balance perturbations. However, the optimal RBT intensity for people with chronic stroke remains unknown. The purpose of this assessor-blinded randomized controlled trial is to investigate the optimal intensity of RBT by comparing high-intensity, moderate-intensity, and walking control groups among 63 individuals with chronic stroke. Participants will undergo four consecutive days of training, with outcomes assessed pre- and post-training and at a one-year follow-up. The primary outcomes are reactive stepping ability, measured using number of steps required to recover balance from a novel perturbation. Secondary outcomes include rates of adverse events, functional balance, falls efficacy, and participation in daily activities. We hypothesize that high-intensity RBT will yield faster adaptations and greater retention compared to moderate-intensity and walking. Determining the optimal RBT intensity could substantially enhance clinical guidelines for stroke rehabilitation, optimize therapy efficiency, and improve patient outcomes by reducing fall risk and improving functional independence. ClinicalTrials.gov ID: NCT06555016.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".