The effectiveness of knee bracing in non‐operative soft tissue and degenerative knee injuries: A systematic review
Bibliographic record
Abstract
PURPOSE: Soft tissue and degenerative knee injuries, including anterior/posterior cruciate ligament (ACL/PCL) injuries, medial knee osteoarthritis (MKOA), and patellofemoral osteoarthritis (PFOA), are common causes of pain and functional decline. Knee bracing is often used as part of non-operative treatment, but its clinical effectiveness remains uncertain. This systematic review aimed to evaluate the role of bracing in improving pain, function, and preventing surgical conversion in adults with non-operatively managed knee injuries. METHODS: A systematic review was conducted following PRISMA guidelines. Embase, Ovid MEDLINE, and Ovid Emcare were searched from inception to March 2025. Studies were included if they reported clinical outcomes of knee bracing in adults with soft tissue or degenerative knee injuries treated non-operatively. Studies focused on biomechanics, prophylaxis, surgery, or paediatric populations were excluded. Data were synthesised narratively with weighted summary statistics. RESULTS: Seventeen studies (706 patients) were included: six on ACL injuries, three PCL, three MKOA, and five PFOA. MKOA studies showed pooled improvements of +14.6 in KOOS Pain and -1.9 in VAS (SDs 1.0 and 0.5). ACL studies reported a pooled Lysholm gain of +11.8 (SD = 4.3), while PCL outcomes showed large single-study improvements (KOOS Pain +31.0, IKDC + 30.0). WOMAC scores improved in MKOA (-13.7) but declined in PFOA (-6.4). Failure (surgical conversion) was reported in 13 studies, with the highest rates in PCL (16.4%) and ACL (10.6%), and the lowest in MKOA (0%) and PFOA (4.0%). Complications were infrequently reported; skin irritation was the most common adverse event. CONCLUSION: Knee bracing demonstrated the most consistent pain and functional improvements in degenerative and PCL injuries. In contrast, outcomes in ACL injuries were more variable and associated with higher failure rates, underscoring the need for injury-specific bracing strategies. These findings emphasise the importance of patient selection, brace design, and early intervention, and support the need for higher-quality studies to guide non-operative management strategies. LEVEL OF EVIDENCE: Level IV.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".