Establishing Consensus on the Treatment of Toxicodendron Dermatitis
Bibliographic record
Abstract
BACKGROUND: Toxicodendron dermatitis (TD) is a common form of allergic contact dermatitis that affects millions of Americans every year. Studies have shown that although there are general recommendations for the treatment of TD, there are no treatment algorithms for clinicians to follow when patients present with TD. OBJECTIVE: The objective of this study was to achieve consensus on the treatment of TD to create practical guidelines for physicians who treat TD. METHODS: Data were collected from March 2020 to April 2021. This study included semistructured focus groups and a Delphi Study with dermatologists to achieve consensus. RESULTS: A total of 51 dermatologists were included in the Delphi. Final agreement with proposed severity criteria ranged from 90.9% to 100.0%. Primary indicators of disease severity were body surface area, presence and severity of pruritus, and anatomic locations of eruptions with 77.4% agreement. Final agreement for the treatment algorithm was over the threshold majority agreement at 67.6%. CONCLUSIONS: Literature guiding the treatment of TD is scarce. The use of the Delphi method and focus groups can help expand dermatological resources both within dermatology and to other specialties that may need to treat skin conditions.
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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 | 0.175 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".