A discrete-choice experiment to assess patients’ preferences for osteoarthritis treatment: An ESCEO working group
Bibliographic record
Abstract
OBJECTIVE: To evaluate the preferences of patients with osteoarthritis for treatment. METHODS: A discrete-choice experiment was conducted among adult OA patients who were presented with 12 choice sets of two treatment options and asked in each to select the treatment they would prefer. Based on literature reviews, expert consultation, patient survey and expert meeting, treatment options were characterized by seven attributes: improvement in pain, improvement in walking, ability to manage domestic activities, ability to manage social activities, improvement in overall energy and well-being, risk of moderate/severe side effects and impact on disease progression. Random parameters logit model was used to estimate patients' preferences and a latent class model was conducted to explore preferences classes. RESULTS: 253 OA patients from seven European countries were included (74% women; mean age 71.3 years). For all seven treatment attributes, significant differences were observed between levels. Given the range of levels of each attribute, the most important treatment attribute in this group was impact on disease progression (29.5%) followed by walking improvement (17.1%) and pain improvement (16.3%). The latent class model identified two preference classes. In the first class (probability of 56%), patients valued impact of disease progression the most (39%). In the second class, walking improvement and improvement in overall energy and well-being were the most important (23%). CONCLUSION: This study suggests that all seven treatment attributes were important for OA patients. Overall, given the range of levels, the most important outcomes were impact on disease progression and improvement in pain and walking.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".