Comparison of benefit–risk preferences of patients and physicians regarding cyclooxygenase-2 inhibitors using discrete choice experiments
Bibliographic record
Abstract
Ji-Hye Byun,1 Sun-Hong Kwon,1 Ji-Eun Lee,1 Ji-Eun Cheon,1 Eun-Jin Jang,2 Eui-Kyung Lee1 1School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do, 2Information Statistics, Andong National University, Andong, Gyeongsangbuk-do, South Korea Purpose: To elucidate and compare benefit–risk preferences among Korean patients and physicians concerning cyclooxygenase-2 (Cox-2) inhibitor treatments for arthritis.Materials and methods: Subjects included 100 patients with arthritis and 60 board-certified orthopedic surgeon physicians in South Korea. Through a systematic review of the literature, beneficial attributes of using Cox-2 inhibitors were defined as a decrease in the Western Ontario and McMaster Universities Arthritis Index for pain score and improvement in physical function. Likewise, risk attributes included upper gastrointestinal (GI) complications and cardiovascular (CV) adverse events. Discrete choice experiments were used to determine preferences for these four attributes among Korean patients and physicians. Relative importance and maximum acceptable risk for improving beneficial attributes were assessed by analyzing the results of the discrete choice experiment by using a conditional logit model.Results: Patients ranked the relative importance of benefit–risk attributes as follows: pain reduction (35.2%); physical function improvement (30.0%); fewer CV adverse events (21.5%); fewer GI complications (13.4%). The physicians’ ranking for the same attributes was as follows: fewer CV (33.5%); pain reduction (32.4%); fewer GI complications (18.1%); physical function improvement (16.0%). Patients were more willing than physicians to accept risks when pain improved from 20% or 45% to 55% and physical function improved from 15% or 35% to 45%.Conclusion: We confirmed that patients and physicians had different benefit–risk preferences regarding Cox-2 inhibitors. Patients with arthritis prioritized the benefits of Cox-2 inhibitors over the risks; moreover, in comparison with the physicians, arthritis patients were more willing to accept the trade-off between benefits and risks to achieve the best treatment level. To reduce the preference gap and achieve treatment goals, physicians must better understand their patients’ preferences. Keywords: discrete choice experiment, Cox-2 inhibitors, willingness to accept, patient and physicians preference, arthritis
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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 | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".