Development and Alpha-testing of a Stepped Decision Aid for Patients Considering Nonsurgical Options for Knee and Hip Osteoarthritis Management
Bibliographic record
Abstract
OBJECTIVE: To develop an innovative stepped patient decision aid (StDA) comparing the benefits and harms of 13 nonsurgical treatment options for managing osteoarthritis (OA) and to evaluate its acceptability and effects on informed decision making. METHODS: Guided by the Ottawa Decision Support Framework and the International Patient Decision Aid Standards, the process involved (1) developing a decision aid with evidence on 13 nonsurgical treatments from the 2012 American College of Rheumatology OA clinical practice guidelines; and (2) interviewing patients with OA and healthcare providers to test its acceptability and effects on knowledge and decisional conflict. RESULTS: The StDA helped make the decision explicit, and presented evidence on 13 OA treatments clustered into 5 steps or levels according to their benefits and harms. Probabilities of benefits and harms were presented using pictograms of 100 faces formatted to allow comparisons across sets of options. It also included a values clarification exercise and knowledge test. Feedback was obtained from 49 patients and 7 healthcare providers. They found that the StDA presented evidence in a clear manner, and helped patients clarify their values and make an informed decision. Some participants found that there was too much information and others said that there was not enough on each treatment option. CONCLUSION: This innovative StDA allows patients to consider both the evidence and their values for multiple options. The findings are being used to revise and plan future evaluation. The StDA is an example of how research evidence in guidelines can be implemented in 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 | 0.030 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".