Proprioceptive neuromuscular facilitation improves pain and descending mechanics among elderly with knee osteoarthritis
Bibliographic record
Abstract
PURPOSE: Knee osteoarthritis (KOA) is a common disease that causes pain and limits functionality in the elderly during daily activities, especially during stair descent. Proprioceptive neuromuscular facilitation (PNF) practices promote multiple-plane joint movements, which relieve pain and increase joint range of motion (ROM). This study aims to examine the effects of a 12-week PNF intervention on pain relief, passive and active joint ROM, external knee adduction moment (KAM), and hip adduction moment (HAM) in the elderly with KOA during stair descent. MATERIALS AND METHODS: Seventy-six elderly who were diagnosed with KOA were assessed for eligibility and, 36 of them met the inclusive criteria, were randomly divided into two groups: the twelve-week PNF intervention group and the control group. Pain score was measured by the Western Ontario and McMaster Universities Arthritis Index (WOMAC). Passive joint ROM was measured using a goniometer. Active joint ROM, KAM, and HAM during stair descent were measured using a motion analysis system with a force platform. All the data were recorded at weeks 0, 6, and 12. RESULTS: Compared to the control group, the PNF group showed a decreased pain score; increased passive hip, knee, and ankle ROM; a decreased minimum knee flexion angle, and increased HAM during stair descent. PERSPECTIVE: Proprioceptive neuromuscular facilitation intervention is a successful method to relieve symptoms of KOA. It relieves pain without increasing KAM, enhances passive ROM, increases active knee flexion ROM, and increases HAM during stair descent in the elderly with KOA.
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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 | 0.000 |
| 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) | 0.001 | 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".