Exploring patient perspectives on EQ-5D-5L data visualization within an individualized decision aid for total knee arthroplasty (TKA) in Alberta, Canada
Bibliographic record
Abstract
Abstract Background Decision aids can help patients set realistic expectations. In this study, we explored alternative presentations to visualise patient-reported outcomes (EQ-5D-5L) data within an online, individualized patient decision aid for total knee arthroplasty (TKA) that, in part, generates individualized comparisons based on age, sex and body mass index, to enhance usability prior to implementation into routine clinical practice. Methods We used data visualization techniques to modify the presentation of EQ-5D-5L outcomes data within the decision aid. The EQ-5D-5L data was divided into two parts allowing patients to compare themselves to similar individuals (1) pre-surgery and (2) 1-year post-surgery. We created 2 versions for each part and sought patient feedback on comprehension, usefulness, and visual appeal. Patients from an urban orthopedic clinic were recruited and their ratings and comments were recorded using a researcher-administered checklist. Data were managed using Microsoft Excel, R version 3.6.1 and ATLAS.ti V8 and analyzed using descriptive statistics and directed content analysis. Results A total of 24 and 25 patients participated in Parts 1 and 2, respectively. Overall, there was a slight preference for Version 1 in Part 1 (58.3%) and Version 2 in Part 2 (64%). Most participants demonstrated adequate comprehension for all versions (range 50–72%) and commented that the instructions were clear. While 50–60% of participants rated the content as useful, including knowing the possible outcomes of surgery, some participants found the information interesting only, were unsure how to use the information, or did not find it useful because they had already decided on a treatment. Participants rated visual appeal for all versions favorably but suggested improvements for readability, mainly larger font and image sizes and enhanced contrast between elements. Conclusions Based on the results, we will produce an enhanced presentation of EQ-5D-5L data within the decision aid. These improvements, along with further usability testing of the entire decision aid, will be made before implementation of the decision aid in routine clinical practice. Our results on patients’ perspectives on the presentation of EQ-5D-5L data to support decision making for TKA treatments contributes to the knowledge on EQ-5D-5L applications within healthcare systems for clinical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".