Checklist for the Systemic Treatment of Psoriasis Using Biologics: A Delphi Study
Bibliographic record
Abstract
BACKGROUND: Despite the complexity of psoriasis treatment using biologic therapy, there does not exist a standardized synoptic reporting form for the initiation of this population. The purpose of this study was to use a modified Delphi approach to develop a standard checklist for the standardized documentation of patients receiving systemic biologic therapy for psoriasis. METHODS: A modified Delphi survey was conducted over 3 rounds (February 2017 through January 2018). An expert panel generated a 51-item checklist that was proposed to participants. Items were rated on an anchored 1-7 Likert scale. Consensus was defined apriori as ≥ 70% agreement by respondents. RESULTS: A total of 58, 17, and 18 dermatologists participated in 3 consecutive Delphi rounds, respectively. Only half of the dermatologists surveyed reported using a checklist for the management of psoriasis. The final checklist comprised 19, 5, 6, and 9 items pertaining to patient history; physical exam and history of systemic therapy; vaccinations; and lab investigations and bloodwork, respectively. CONCLUSIONS: Given the increasing availability and complexity of biologic agents for psoriasis treatment, there is a need to promote standardized documentation for this population. The Checklist for the Systemic Treatment of Psoriasis presents 38 items that should be considered when initiating patients with psoriasis on biologic therapy.
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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 | 0.157 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".