Usability testing of an individualized decision aid for total knee arthroplasty
Bibliographic record
Abstract
Osteoarthritis (OA) is a leading cause of total knee arthroplasty (TKA), affecting over 15 % of Canadians. With an aging population and suboptimal use of non-surgical options, TKA rates and wait times are rising. Although TKA is effective, 30 % of patients are dissatisfied due to unmet expectations, suggesting some surgeries may be inappropriate. Patient decision aids can set realistic expectations, improve decision quality, and enhance satisfaction. We developed an individualized online patient decision aid allowing patients to compare treatment outcomes based on similar characteristics (age, sex and body mass index) and evaluated its usability before clinical implementation. Participants were recruited from a high-volume urban hip and knee clinic. Eligible adults diagnosed with knee OA completed the decision aid online and subsequently filled out demographics and survey forms, including the Preparation for Decision Making Scale (PDMS), System Usability Scale (SUS), and Acceptability Scale. Data were analyzed using descriptive statistics and content analysis of open-ended responses. There were 20 participants (mean age 68 years, 65 % female). The average PDMS score was 66.4, indicating above-average preparedness for decision-making. The SUS score averaged 63.4, suggesting marginal usability. Females and participants under 70 years reported higher PDMS and SUS scores. Most participants rated the information presentation as “good” or “excellent,” with 75 % finding the decision aid's length appropriate and information balanced. Feedback highlighted the need to simplify content, reduce variables, and offer the aid earlier in treatment. The decision aid demonstrated reasonable usability, acceptability, and usefulness for routine practice. Future research should explore its impact on long-term patient outcomes and satisfaction, including among non-surgical populations. Incorporating this decision aid into routine practice can help patients set realistic expectations and make informed decisions, reducing dissatisfaction. Offering it earlier in the patient journey may enhance its impact, especially for non-surgical options. • Osteoarthritis (OA) is a leading cause of total knee arthroplasty (TKA). • Some patients are dissatisfied with TKA due to unmet expectations. • Patient decision aids can set realistic expectations, improve decision quality, and enhance satisfaction. • An individualized decision aid shows reasonable usefulness, acceptability, and usability. • Offering the decision aid earlier may boost impact, especially for non-surgical options.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".