Support Needed to Involve Psoriasis Patients in Treatment Decisions: Survey of Dermatologists
Bibliographic record
Abstract
BACKGROUND: Little is known about the interaction between dermatologists and their patients in facilitating treatment decisions for psoriasis. PURPOSE: Our objective was to determine dermatologists' perceptions of the needs of psoriasis patients in treatment decisions. METHODS: Dermatologists were invited to complete an 18-item online survey on the treatment of psoriasis, including questions on decision-making roles, factors they considered important to patients in treatment decisions, and patients' needs for decision support. RESULTS: Seventy dermatologists completed the survey (15% response rate). The highest rated factors in decision making were access to physicians for discussion (86%) and information about the risks and benefits (80%); the latter was more frequently reported by those ≥ 50 years (p = .021). Treatment-specific factors of greatest importance were side-effect profile (87%) and cost (80%). Potential hindrances were patient misconceptions about disease, inadequate patient education materials, patient indecision, and inadequate physician time. CONCLUSION: Although dermatologists consider accessibility to dermatologists and information on treatment risk and benefits to be important in treatment decision making, they report time with patients and educational materials to be inadequate. LIMITATIONS: The small sample size may limit the generalizability of our findings.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".