Efeitos da tarefa subsequente ao sentar e levantar sobre a ativação muscular e início do movimento
Bibliographic record
Abstract
Muscle activation (activation time) and the beginning of movement (motor reaction time) can be changed depending on the complexity of the task. The objectives of this study were to compare the time for activation of the paraspinal and the vastus lateralis muscles, and the motor reaction time during the execution of the tasks sit-to-stand (STS) and sit-to-walk (STW), which includes the execution of the subsequent task of gait initiation. Twelve healthy young subjects participated in the study. They performed two tasks(STS and STW), five times each, randomly, separated by two minutes of rest. The kinematics of the movement were recorded using a digital electrogoniometer attached to the hip joint and muscle activation using surface electromyographyin both muscles. The average of the five repetitions was calculated for each task. The beginning of the task was signaled by a luminous device, which was also used to identify the initial point for calculating the activation time andmotor reaction time. Both muscles showed a longer latency for the activation time and motor reaction time during the STW task when compared with STS. Basedon these results, it can be concluded that both the postural (paraspinal) and prime mover muscles (vastus lateralis) undergo change in the motor programming during the execution of the STS task when a subsequent task (gait initiation) is included. Motor programming is dependent on task complexity, where a more complex task (STW) will result in delays of movement programming and execution.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".