Optimizing the management of psoriasis in patients with skin of color: A Canadian Delphi consensus
Bibliographic record
Abstract
Background: There is limited evidence on treating psoriasis patients with skin of color (SOC), contributing to disparities in accessing appropriate care for these patients. Objectives: This study aimed to develop consensus statements defining SOC terminology and addressing needs to optimize the clinical management of psoriasis in patients with SOC. Methods: Using the modified Delphi methodology 16 Canadian dermatologists with expertise in psoriasis developed consensus statements. Four core faculty members drove the content of the study, and 12 additional panel members were consulted to vote and provide consensus on the content produced by the core faculty. At a final meeting, the full panel revised and voted on the final consensus statements. Results: The exercise resulted in 11 consensus statements on SOC terminology, as well as 5 primary and 4 secondary statements on clinical presentation and differential diagnosis, and treatment guidelines based on evidence and expert opinion. Four additional consensus statements on current assessment tools and access to care were developed based solely on expert opinion. Limitations: The available evidence was limited, low quality, and inappropriate for formal quality assessment. Conclusions: The consensus statements developed in this study may provide valuable guidance to the dermatology community treating psoriasis patients with SOC.
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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 | 0.163 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".