Treatment Decision Needs of Psoriasis Patients: Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: Informed shared decision making is a mutual process engaging both doctor and patient and informed by best medical evidence and patient values and preferences. OBJECTIVE: Our aim was to identify the needs of psoriasis patients in decisions on selecting treatment. METHODS: Psoriasis subjects participated in an online survey on decisional role, postdecisional conflict, and treatment awareness. RESULTS: Of 2,622 people invited to participate, 248 completed surveys. Their most recent treatment decision was either made by subjects alone (42%) or physicians alone (28%) or was shared (29%). Subjects perceived that their doctors lacked time to stay abreast of treatments, to provide counseling, and to access appropriate treatments. Deficiencies most frequently identified were information on options, clarification of values, access to physicians, and decision-making skills. Those with a body surface area (BSA) ≥ 3% more frequently indicated that having the skill or ability to make treatment decisions was important. LIMITATIONS: The limitations of this study include sampling, recall, and reporting bias. Percent BSA was not verified. CONCLUSIONS: The multiple deficiencies in support of psoriasis patients in treatment decisions may preclude informed shared decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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) | 0.002 | 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".