Community‐based exercise program reduces chronic knee pain in elderly Japanese women at high risk of requiring long‐term care: A non‐randomized controlled trial
Bibliographic record
Abstract
AIM: To estimate the prevalence of low back pain and/or knee pain among the elderly at high risk of requiring long-term care, and to determine the effectiveness of a community-based exercise program provided in accordance with the Motor Function Improvement Program for improving low back and/or knee pain. METHODS: The target population of this study was 320 residents aged ≥65 years who were eligible for the exercise program. For the intervention group, weekly exercise classes of 120 min duration were held 12 times over 3 months. The main outcome measures were changes between the baseline and 3-month follow up in visual analog scale (VAS) scores for pain and in the Western Ontario McMaster Osteoarthritis Index (WOMAC) pain for severity of knee pain. RESULTS: The number of participants reporting chronic low back and/or knee pain was 252 with a prevalence of 78.8%. Among them, 68 who were allocated to the intervention group and 125 to the control group completed the study, and were stratified by sex. In women, change in the VAS scores of low back pain was -17.5±23.2 for the intervention group and -7.2±23.4 for the control group (between-group differences P=0.03). For knee pain, significant changes in the VAS scores (between-group differences P=0.04) and WOMAC pain (P<0.001) were observed; -14.9±24.9 and -0.6±3.1 for the intervention group, and -0.2±28.5 and 2.2±3.2 for the control group, respectively. No significant difference was observed in men. CONCLUSION: Community-based exercise programs might reduce prevalent knee pain in elderly women at high risk of requiring long-term care.
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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) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".