Efeitos do treino em esteira na marcha com dupla tarefa de indivíduos com doença de Parkinson: ensaio clínico controlado randomizado
Bibliographic record
Abstract
Background: The gait automaticity loss difficults realization of concurrent activities - Dual Task (DT). In these situations, individuals with Parkinson`s disease (PD) show a significant reduction in gait velocity and stride length, as strides variability and asymmetry increased, factors predisposing to falls. However, recent studies have shown that training involving DT may cause subsequent improvements in gait variables with DT in individuals with PD. The treadmill use was adopted by this study, by promoting greater regularity in step and enhance training. Objective:To investigate immediate effects of gait training associated with cognitive tasks on gait in individuals with PD. Methods: Twenty-two volunteers were randomly divided into two groups: control group (n = 11), who performed gait training on a treadmill for 20 minutes, and the experimental group (n = 11), who performed treadmill gait training for 20 minutes associated with cognitive tasks of verbal fluency, memory, and spatial planning. Participants were evaluated in phase on of antiparkinsonian medication as the demographic, clinical and anthropometric (identification form), cognitive status (Montreal Cognitive Assessment - MoCA), executive function (Frontal Assessment Battery), level of physical disability (Hoehn and Yahr Modified), motor and functional status (Unified Rating Scale for Parkinson`s Disease - UPDRS), and kinematics (Qualisys Motion Capture System). Results: There were not differences between groups, but both showed improvement after the intervention. The control group had an increase in velocity (p = 0.008), stride length (p = 0.04), step length (p = 0.02) and decreased double support time(p = 0.03). The experimental group showed an increase in speed (p = 0.002), stride length (p = 0.008), step length (p = 0.02) and cadence (p = 0.01), as well as a decrease in the width stride (p = 0.001) and total support time (p = 0.02). As the angular variables, the experimental group had a significant increase in the initial contact angle of ankle (p = 0.01). Conclusion: The gait training combined with cognitive activities didn`t provide significant improvements in gait variables with DT, but this study was the first to demonstrate that gait training on treadmill as simple task minimized the negative interference of DT in PD
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".