Evaluation of psoriasis patients’ attitudes toward benefit–risk and therapeutic trade-offs in their choice of treatments
Bibliographic record
Abstract
OBJECTIVE: Treatment options for psoriasis offer trade-offs in terms of efficacy, convenience, and risk of adverse events. We evaluated patients' preferences with respect to benefit-risk in the treatment of psoriasis. METHODS: A discrete choice experiment was conducted in adults from the UK with moderate-to-severe psoriasis using an orthogonal design with 32 hypothetical choice sets. Participants were randomly assigned to one of two surveys with 16 choice sets. Patients' preferences were investigated with respect to the following attributes: reduction in body surface area affected by psoriasis, treatment administration (frequency and mode of delivery), short-term diarrhea or nausea risk, and 10-year risk of developing melanoma or nonmelanoma skin cancer, tuberculosis, or serious infections. A mixed effects logistic regression model generated relative preferences between treatment profiles. RESULTS: Participants (N=292) had a strong preference to avoid increased risk of melanoma or nonmelanoma skin cancer (odds ratio [OR]: 0.44 per 5% increased 10-year risk) and increased risks of tuberculosis and serious infections (both ORs: 0.73 per 5% increased 10-year risk) and preferred once-weekly to twice-daily tablets (OR: 0.76) and weekly (OR: 0.56) or fortnightly (OR: 0.65) injections. Participants preferred avoiding treatments that may cause diarrhea or nausea in the first 2 weeks (OR: 0.87 per 5% increase) and preferred treatments that effectively resolved plaque lesions (OR: 0.93 for each palm area still affected). CONCLUSION: All attributes were significant predictors of choice. Patients' preference research complements clinical trial data by providing insight regarding the relative weight of efficacy, tolerability, and other factors for patients when making treatment choices.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".