Conservative Treatments, Surgical Treatments, and the KineSpring® Knee Implant System for Knee Osteoarthritis: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Knee osteoarthritis (OA) is a disease with a high global burden, and multiple treatment options are available. In the current review we summarize the results of studies that have evaluated treatments of knee OA, and we compare these results with an implantable load absorber called the KineSpring® Knee Implant System. METHODS: We conducted a literature search of systematic reviews on treatment strategies for knee OA. We pooled results for each treatment in three categories: pain, function, and stiffness. Then we compared this data to that available for the KineSpring System. RESULTS: Medications and viscosupplementation show promising initial pain relief for knee OA. Aerobic and resistance training, unicompartmental knee arthroplasty (UKA), and total knee arthroplasty (TKA) showed a reduction in pain scores. High tibial osteotomy (HTO) generally improves pain and function at 6 weeks, but long-term results are lacking. The KineSpring System demonstrated significant improvements from baseline to 24 months, but direct comparative data are lacking. CONCLUSIONS: Evidence for knee OA therapies suggests improved pain, stiffness, and functional outcomes. Additional research is necessary to clearly delineate the advantages of various approaches to guide practice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".