What is clearance worth? Patients’ stated risk tolerance for psoriasis treatments
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to provide quantitative evidence of patients' tolerance for therapeutic risks associated with psoriasis treatments that could offer psoriasis improvements beyond the PASI 75 benchmark. MATERIALS AND METHODS: We used a discrete-choice experiment in which respondents chose between competing psoriasis treatments characterized by benefits (i.e. reduced plaque severity, reduced plaque area), risks (i.e. 10-year risk of tuberculosis, 10-year risk of death from infection), and treatment regimen. We analyzed choice data using random-parameters logit models for psoriasis affecting the body, face, or hands. RESULTS: Of 927 eligible members of the National Psoriasis Foundation who completed the survey, 28% were unwilling to accept any greater risk of treatment-related infection mortality. Among the remaining 72%, respondents were willing to accept higher risks of infection-related mortality associated with treatment to completely remove plaques covering only 1% of the body, compared to reducing lesions from 10 to 1% of the affected area. This finding was more pronounced for lesions on the face. CONCLUSIONS: Most patients placed greater value on eliminating even very small plaques compared to avoiding treatment-related risks. The perceived importance of complete versus near-complete clearance was stronger than previously documented.
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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 | 0.007 | 0.038 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".