Effect of Ankle Plantar Flexor Spasticity Level on Balance in Patients With Stroke: Protocol for a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The lower limb spasticity after stroke can affect the balance and gait of patients with stroke. OBJECTIVE: The aim of this study is to assess the effects of ankle plantar flexor spasticity level on balance in patients with stroke. METHODS: Patients with stroke were recruited from neurology and physiotherapy clinics in Tehran, Iran. Based on the level of ankle plantar flexor spasticity according to the Modified Modified Ashworth Scale (MMAS), the eligible patients with stroke were divided into 2 groups: high spasticity (MMAS score≥2) and low spasticity (MMAS score<2). The primary outcome measures were the MMAS scores, Activities-Specific Balance Confidence questionnaire scores, eyes-open and eyes-closed posturography measures, and Timed Up and Go test results. The secondary outcome measures were the ankle passive range of motion and ankle joint proprioception. The t test, mixed model univariate analysis of variance, and Spearman rank correlation were used for statistical analysis. RESULTS: Data collection and statistical analysis are complete. The interpretation of results is underway. We expect the results to be published in winter 2020. CONCLUSIONS: We believe that patients with high ankle plantar flexor spasticity after stroke will demonstrate greater balance dysfunction, which will worsen with impaired proprioception, passive range of motion, and eyes closed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/16045.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".