How Do Physicians Weigh Benefits and Risks Associated with Treatments in Patients with Osteoarthritis in the United Kingdom?
Bibliographic record
Abstract
OBJECTIVE: To quantify the relative importance that UK physicians attach to the benefits and risks of current drugs when making treatment decisions for patients with osteoarthritis (OA). METHODS: Physicians treating at least 10 patients with OA per month completed an online discrete-choice experiment survey and answered 12 treatment-choice questions comparing medication profiles. Medication profiles were defined by 4 benefits (reduction in ambulatory pain, resting pain, stiffness, and difficulty doing daily activities) and 3 treatment-related risks [bleeding ulcer, stroke, and myocardial infarction (MI)]. Each physician made medication choices for 3 of 9 hypothetical patients (varied by age, history of MI, hypertension, and history of gastrointestinal bleeding). Importance weights were estimated using a random-parameters logit model. Treatment-related risks physicians were willing to accept in exchange for various reductions in ambulatory and resting pain also were calculated. RESULTS: The final sample was 475. A reduction in ambulatory pain from 75 mm to 25 mm (1.6 units) was 1.1 times as important as an increase in MI risk from 0% to 1.5% (1.5 units). The greatest importance was for eliminating a 3% treatment-related risk of MI or stroke. On average, physicians were willing to accept an increase in bleeding ulcer risk of 0.7% (95% CI 0.4%-1.7%) for a reduction in ambulatory pain of 75 mm to 50 mm. CONCLUSION: When presented with well-known benefits and risks of OA treatments, physicians placed greater importance on the risks than on the analgesic properties of the drug. This has implications for the reporting of the results of clinical research to physicians.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".