Five-Year Follow-up of Knee Joint Distraction: Clinical Benefit and Cartilaginous Tissue Repair in an Open Uncontrolled Prospective Study
Bibliographic record
Abstract
Objective In end-stage knee osteoarthritis, total knee arthroplasty (TKA) may finally become inevitable. At a relatively young age, this comes with the risk of future revision surgery. Therefore, in these cases, joint preserving surgery such as knee joint distraction (KJD) is preferred. Here we present 5-year follow-up data of KJD. Design Patients ( n = 20; age <60 years) with conservative therapy resistant tibiofemoral osteoarthritis considered for TKA were treated. Clinical evaluation was performed by questionnaires. Change in cartilage thickness was quantified on radiographs and magnetic resonance images (MRI). The 5-year changes after KJD were evaluated and compared with the natural progression of osteoarthritis using Osteoarthritis Initiative data. Results Five-years posttreatment, patients still reported clinical improvement from baseline: ΔWOMAC (Western Ontario and McMaster Universities Arthritis Index) +21.1 points (95% CI +8.9 to +33.3; P = 0.002), ΔVAS (visual analogue scale score) pain -27.6 mm (95%CI -13.3 to -42.0; P < 0.001), and minimum radiographic joint space width (JSW) of the most affected compartment (MAC) remained increased as well: Δ +0.43 mm (95% CI +0.02 to +0.84; P = 0.040). Improvement of mean JSW (x-ray) and mean cartilage thickness (MRI) of the MAC, were not statistically different from baseline anymore (Δ +0.26 mm; P = 0.370, and Δ +0.23 mm; P = 0.177). Multivariable linear regression analysis indicated that KJD treatment was associated with significantly less progression in mean and min JSW (x-ray) and mean cartilage thickness (MRI) compared with natural progression (all Ps <0.001). Conclusions KJD treatment results in prolonged clinical benefit, potentially explained by an initial boost of cartilaginous tissue repair that provides a long-term tissue structure benefit as compared to natural progression. Level of evidence, II.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".