Midterm Outcomes and Predictors of Clinical Success with the KineSpring Knee Implant System
Bibliographic record
Abstract
The purpose of this study was to (1) evaluate joint pain and function in knee osteoarthritis (OA) patients treated with a joint-sparing, extracapsular implant and (2) identify patient characteristics that influenced clinical outcomes. This study included 99 patients with symptomatic medial knee OA refractory to conservative care who were treated with the KineSpring Knee Implant System and followed for a mean of 17 months (range, 1.5 to 48 months). All devices were successfully implanted and activated with no intraoperative complications. Statistically significant mean improvements of 56%, 50%, and 38% were observed for Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain, Function, and Stiffness scales, respectively (all P < 0.001). Regardless of gender, age group, body mass index classification, or disease severity, all WOMAC domain scores significantly improved during the postoperative follow-up period. WOMAC clinical success rates were 77.8% for Pain, 77.8% for Function, and 68.7% for Stiffness. Neither gender, age group, body mass index classification, nor disease severity predicted clinical success in any WOMAC domain. In conclusion, the KineSpring System yields clinically meaningful improvements in joint pain and function in patients with medial knee OA. Additionally, unlike joint-altering procedures such as knee arthroplasty or high tibial osteotomy, patient characteristics had little association with postoperative clinical outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".