Is Knee Joint Distraction a Viable Treatment Option for Knee OA?—A Literature Review and Meta-Analysis
Bibliographic record
Abstract
Knee joint distraction (KJD) is a new application of an established technique to regenerate native cartilage using an external fixator. The purpose of this study is to perform a systematic review and meta-analysis of the literature to determine whether KJD is beneficial for knee osteoarthritis and how results compare with established treatments. Studies assessing the outcomes of KJD were retrieved, with three studies (one cohort and two randomized controlled trials), 62 knees, meeting the inclusion criteria. The primary outcome was functional outcome, assessed using a validated outcome score, at 1 year. Secondary outcomes included pain scores, structural assessment of the joint, and adverse events. KJD is associated with improvements in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) from baseline to 1 year as well as reductions in pain scores and improvements in structural parameters assessed radiographically and by magnetic resonance imaging. KJD is not associated with decreased knee flexion, but is associated with a high risk of pin site infection. In patients aged 65 years or under at 1 year, no differences in WOMAC or pain scores was detected between patients managed with KJD compared with high tibial osteotomy or total knee arthroplasty. KJD may represent a potential treatment for knee arthritis, though further trials with longer term follow-up are required to establish its efficacy compared with contemporary treatments. This is a Level I (systematic review and meta-analysis) study.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".