Unlocking the Secrets of Knee Joint Unloading: A Systematic Review and Biomechanical Study of the Invasive and Non-Invasive Methods and Their Influence on Knee Joint Loading
Bibliographic record
Abstract
Background/Objectives: This review analyzes the effects of invasive and non-invasive methods of knee joint unloading on knee loading, employing a biomechanical model to evaluate their impact. Methods: PubMed, Web of Science, Cochrane, and Scopus were searched up to 15 May 2024 to identify eligible clinical studies evaluating Joint Space Width, Cartilage Thickness, the Western Ontario and McMaster Universities Osteoarthritis Index, the Knee Injury and Osteoarthritis Outcome Score system, Gait velocity, Peak Knee Adduction Moment, time to return to sports and to work, ground reaction force, and the visual analogue scale pain score. A second search was conducted to select a biomechanical model that could be parametrized, including the modifications that each treatment would impose on the knee joint and was capable of estimate joint loading to compare the effectiveness of each method. Results: Analyzing 28 studies (1652 participants), including 16 randomized clinical trials, revealed significant improvements mainly when performing knee joint distraction surgery, increasing Joint Space Width even after removal, and high tibial osteotomy, which realigns the knee but does not reduce loading. Implantable shock absorbers are also an attractive option as they partially unload the knee but require further investigation. Non-invasive methods improve biomechanical indicators of knee joint loading; however, they lack quantitative analysis of cartilage volume or Joint Space Width. Conclusions: Current evidence indicates a clear advantage in knee joint unloading methods, emphasizing the importance of adapted therapy. However, more extensive research, particularly using non-invasive approaches, is required to further understand the underlying knee joint loading mechanisms and advance the state of the art.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| 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) | 0.003 | 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".