Treatment Preferences of Patients with Early Knee Osteoarthritis: A Decision Board Analysis Assessing High Tibial Osteotomy versus the KineSpring® Knee Implant System
Bibliographic record
Abstract
INTRODUCTION: Decision boards can be useful in shared decision making by helping patients and their physicians choose among treatment options. Two surgical treatments for early knee osteoarthritis (OA) are high tibial osteotomy (HTO) and the KineSpring® Knee Implant System. The primary objective of this study was to determine patient preferences between these two treatments using a decision board. METHODS: We developed a decision board that presented information on HTO and the KineSpring System for treating knee OA. First, it was presented to 15 individuals for a pilot test and a "scope test." Then it was presented to 81 individuals who were asked to imagine that they had early to midstage knee OA, and this group was administered a complete a series of questions, including their treatment preference and what they would be willing to pay if they elected to use the KineSpring System. Descriptive statistics were calculated and a chi-squared test was conducted to assess any significant differences in patient preferences based on demographic characteristics. RESULTS: Our pilot test confirmed that most participants (87%) agreed that the decision board was easy to understand and helped them in making a decision. Of 81 respondents, the KineSpring System was preferred by 60% (n = 49). Individuals selecting KineSpring would be willing to pay an average of $2,700 to receive it over HTO. CONCLUSIONS: When provided with treatment options and information, 60% of individuals preferred the KineSpring System over HTO. The decision board was well-received as a useful tool for presenting information.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".