Effects of cycling rehabilitation training on patients with knee osteoarthritis: A systematic review and meta‑analysis
Bibliographic record
Abstract
Abstract Studies have shown that individuals with knee osteoarthritis (KOA) may benefit from cycling exercise. However, the supportive evidence remains unclear. This systematic review aimed to investigate the effects of cycling rehabilitation training (CRT) on the recovery of osteoarthritic knee joints. Five databases were searched with publishing date restrictions from 1 January 2000 to 1 March 2022. We included studies that 1) recruited participants with KOA, 2) used CRT in the intervention, 3) compared measurements before and after the intervention or between a KOA group and a healthy group, and 4) included the measurements of interest. The quality of the studies was assessed using the modified Downs and Black checklist. A random-effects meta-analysis of Western Ontario and McMaster Universities Arthritis index, Lequesne index, and Timed Up and Go test scores was performed. The changes in muscle strength, kinetics, and kinematics as a result of the intervention were summarised. The quality of the 19 included studies was moderate with a median quality score of 19.05. CRT improved muscle strength and physical function (SMD 0.94, 95% CI [0.66, 1.22]), and reduce pain (SMD 0.94, 95% CI [0.66, 1.22]) and joint stiffness (SMD 0.74, 95% CI [0.46, 1.01]) in KOA patients. Compared with healthy subjects, KOA patients showed increased extensor moments and abduction peak adduction angles of their knee joints, and decreased internal rotation moment and peak angles of knee flexion and extension. CRT was effective in relieving knee pain, restoring motor function, and improving lower limb muscle strength. Knee abduction moment may be an indicator of rehabilitation progress.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".