A Canadian Self-Administered Online Survey to Evaluate the Impact of Moderate-to-Severe Psoriasis among Patients
Bibliographic record
Abstract
BACKGROUND: Few population studies of individuals living with psoriasis have been performed in Canada. OBJECTIVE: The objective of this survey was to understand the severity and impact of psoriasis on the lives of Canadian patients. METHODS: An online survey was conducted using a consumer panel. Eligible subjects reported a diagnosis of psoriasis and provided a self-reported level of severity. In addition, subjects had to either (a) have psoriasis covering at least 3% of their body surface area; (b) have psoriasis on a sensitive area of the body; or (c) be currently undergoing treatment for their psoriasis with systemic medication and/or phototherapy. RESULTS: A total of 514 panelists met the inclusion criteria and completed the survey. Current moderate, severe, or very severe psoriasis was reported by 65% of respondents. Nearly all subjects (96%) had psoriasis affecting a sensitive area of the body. At the time of the survey, 18% were taking systemic medication and/or phototherapy. Comorbidities, such as obesity and high blood pressure, were highly prevalent, with 75% of respondents reporting at least one other diagnosis. Data from the SF-8 and Dermatology Life Quality Index instruments indicated that psoriasis negatively impacted quality of life. CONCLUSION: Moderate-to-severe psoriasis places a burden on Canadian patients, some of whom may be receiving suboptimal treatment or treatment not appropriate for the severity of their condition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".