Facing the decision about the treatment of hip or knee osteoarthritis: What are patients’ needs?
Bibliographic record
Abstract
PURPOSE: There is an increasing interest in modern orthopaedic practice to empower patients to participate in shared decision-making. Decision aids are thought to be helpful in this process. Before creating decision aids for patients with osteoarthritis in the knee or hip, the goal was to identify the needs of patients and physicians when deciding about the treatment. Specifically, this study tested the null hypothesis that there is no significant difference in decisional conflict between patients with knee or hip osteoarthritis and orthopaedic surgeons. METHODS: Thirty-three orthopaedic surgeons and 172 patients with either knee or hip osteoarthritis were surveyed. Patients entered their demographic information and completed the Knee Injury and Osteoarthritis Outcome Score/Hip Disability and Osteoarthritis Outcome Score, the Assessment of Needs survey based on the Ottawa Decision Support Framework, the Decisional Conflict Scale, the Pain Self-Efficacy Questionnaire, the Patient-Doctor Relationship Questionnaire-9, the Pain Anxiety Symptoms Scale and the Patient Health Questionnaire. Physicians entered their demographic and professional information and completed the Assessment of Needs survey based on the Ottawa Decision Support Framework and the Decisional Conflict Scale. RESULTS: The results showed that there was a significant difference (P < 0.01) between patients [mean (SD), 33 (19)] and physicians [mean (SD), 24 (14)], regarding decisional conflict about the treatment of knee and hip osteoarthritis. It also showed that patients' decisional conflict was associated with the patient-doctor relationship, and there was a need for information and clearness of one's values for risks and benefits. CONCLUSION: Patients had a higher magnitude of decisional conflict than physicians and that the level of decisional conflict was positively influenced by the patient-doctor relationship. Patients had high needs regarding information and clearness of one's values for risks and benefits. LEVEL OF EVIDENCE: II.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".