Effects of High-Intensity Strength Training in Adults With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to assess the effects of high-intensity strength training versus low-intensity strength training or routine care in adults with knee osteoarthritis. DESIGN: PubMed, Embase, Cochrane Library, and Web of Science were searched up to March 10, 2021. The outcomes were knee pain, knee function, quality of life, and adverse events. RESULTS: Ten studies of 892 subjects with knee osteoarthritis were included. No significant differences were found between the high-intensity strength training group and the low-intensity strength training or routine care group in the Western Ontario and McMaster Universities Osteoarthritis Index pain score, Knee Injury and Osteoarthritis Outcome Score pain score, Western Ontario and McMaster Universities Osteoarthritis Index stiffness score, Western Ontario and McMaster Universities Osteoarthritis Index physical function score, Knee Injury and Osteoarthritis Outcome Score symptom score, Knee Injury and Osteoarthritis Outcome Score activities of daily living score, Knee Injury and Osteoarthritis Outcome Score sport and recreation score, Timed Up and Go result, gait velocity, walking time, peak torque of the knee extensors, Knee Injury and Osteoarthritis Outcome Score quality of life score, and adverse event incidence (all P > 0.05). The peak torque of the knee flexors at 120-degree per sec contraction (pooled weighted mean difference, 7.520; 95% confidence interval, 1.256 to 13.784; P = 0.019) in the high-intensity group was improved significantly than that in the low-intensity training or routine care group. CONCLUSIONS: High-intensity strength training may have similar effects in improving knee pain, knee function, and quality of life, with comparable safety to low-intensity strength training and routine 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".