The Effects of Neuroscience-Based Sensorimotor Exercise Training Using Laser Pointer in Elderly with Chronic Knee Pain: Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Neuroscience-based sensorimotor exercise training using laser pointer (nbset) is a new aproach exercise training method. We aimed to investigate the effects of nbpet using laser pointer on different functional parameters. In this randomized controlled study, we recruited 19 ( 14 females, 5 males) participants with chronic knee pain. Participants were divided into two groups (experimental (n=12), control (n=7)). Joint position errors were evaluated with the joint position error assessment method, range of motion with a goniometer, kinesiophobia with the Tampa kinesiophobia scale, pain with the McGill pain scale, quality of life with the Older People Quality of Life questionnaire (OPQoL-brief), and mobility with the time up and go test. Both groups were included in the exercise program with proprioceptive exercise training for 2 sessions per week for 6 weeks. In addition, the experimental group was trained with nbset using laser pointer at the same time. We found significant differences between groups in terms of joint position error (horizontal p=0.026 and global p=0.012), quality of life (p=0.022) and mobility (p=0.005) in favor of the experimental group. Although there were improvements in the groups in terms of range of motion (p=0.261), kinesiophobia (p=0.098) and pain (p=0.446) scores, there was no significant difference between the groups. Nbset using laser pointer may reduce joint position error, improve quality of life and mobility in elderly with chronic knee pain. Long-term studies with large samples are needed to determine the effects on range of motion, kinesiophobia, and pain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".