Improved Decisional Conflict and Preparedness for Decision Making Using a Patient Decision Aid for Treatment Selection in Psoriasis: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: We developed a patient decision aid (pDA) to assist psoriasis patients in treatment decisions. OBJECTIVE: This pilot study evaluated the pDA in patient knowledge, decisional conflict, and preparation for decision making. METHODS: Newly referred psoriasis patients in a private dermatology office completed self-administered surveys at three time points: before (visit 1) and on two occasions after provision of the pDA (visit 2 up to 2 weeks after visit 1; visit 3 up to 6 weeks after visit 1). The survey included questions regarding knowledge of psoriasis and its treatment and validated questionnaires on decisional conflict and preparation for decision making. RESULTS: Ten psoriasis patients participated (seven men, three women; mean age 45.7 years), with a mean age of 11.4 years since diagnosis. Improvement by visit 3 was observed for knowledge (p = .06), reduced decisional conflict (p ≤ .001), and preparation for decision making (p ≤ .05). Patients tended to self-select treatment appropriate to the level of psoriasis severity. CONCLUSION: This pilot study of the pDA showed improved patient knowledge of psoriasis and its treatments, reduced decisional conflict, and increased patient preparation for decision making. LIMITATIONS: This small study was not randomized and did not have a comparator arm.
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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 | 0.005 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".