Joint unloading implant modifies subchondral bone trabecular structure in medial knee osteoarthritis: 2-year outcomes of a pilot study using fractal signature analysis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (OA) is largely attributable to chronic excessive and aberrant joint loading. The purpose of this pilot study was to quantify radiographic changes in subchondral bone after treatment with a minimally invasive joint unloading implant (KineSpring(®) Knee Implant System). METHODS: Nine patients with unilateral medial knee OA resistant to nonsurgical therapy were treated with the KineSpring System and followed for 2 years. Main outcomes included Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, function, and stiffness subscores and independent core laboratory determinations of joint space width and fractal signature of the tibial cortex. RESULTS: WOMAC scores, on average, improved by 92% for pain, 91% for function, and 79% for stiffness over the 2-year follow-up period. Joint space width in the medial compartment of the treated knee significantly increased from 0.9 mm at baseline to 3.1 mm at 2 years; joint space width in the medial compartment of the untreated knee was unchanged. Fractal signatures of the vertically oriented trabeculae in the medial compartment decreased by 2.8% in the treated knee and increased by 2.1% in the untreated knee over 2 years. No statistically significant fractal signature changes were observed in the horizontally oriented trabeculae in the medial compartment or in the horizontal or vertical trabeculae of the lateral compartment in the treated knee. CONCLUSION: Preliminary evidence suggests that the KineSpring System may modify knee OA disease progression by increasing joint space width and improving subchondral bone trabecular integrity, thereby reducing pain and improving joint function.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".