Understanding Preferences for Disease‐Modifying Drugs in Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Numerous disease-modifying drugs for osteoarthritis (DMOADs) are under investigation. However, patients' preferences for drugs to prevent progression of OA are not known. The objective of this study was to quantify patient preferences for potential DMOADs. METHODS: We administered a conjoint analysis survey to 304 patients attending outpatient general medicine and specialty clinics. All patients seated in the waiting rooms were asked if they would participate in a survey to elicit opinions about arthritis treatments. We performed simulations to estimate preferences for 4 options to prevent worsening of knee OA: best case (pill, highest benefit, lowest risk, lowest cost), worst case (infusion, lowest benefit, highest risk, highest cost), moderate subcutaneous injection (injection, mid-level benefit, mid-level risk, mid-level cost), and moderate infusion (same as subcutaneous injection except administered by infusion). RESULTS: Subjects' median age was 57 years; 55% were women and 76% were white. Segmentation analyses revealed 4 patterns of preferences. A minority (5%) did not want to perform subcutaneous injections and would only consider DMOADs under the best-case scenario. Approximately 20% were risk sensitive and were willing to take DMOADs under the best-case scenario, but would start rejecting these medications as risk increased. A significant number rejected DMOADs under all conditions (16.4%); however, the largest segment (59.2%) had a strong preference for DMOADs across all scenarios. CONCLUSION: Our results suggest that a significant percentage of a nonselected outpatient population might be willing to accept at least a moderate degree of risk in order to prevent worsening knee OA.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".