Understanding the atopic dermatitis-psoriasis phenotypic switch through a mechanistic epidemiology approach
Bibliographic record
Abstract
Abstract Atopic Dermatitis (AD) and psoriasis (PsO) are two frequent dermatologic conditions that may co-occur in a cluster of patients, yet current understanding of how these two conditions relate to one-another remains poorly understood. One way to better understand their relationship is through a process called phenotypic switching, where AD and PsO can turn into one another. We utilized a pharmacovigilance-based epidemiological approach to better understand this phenomenon. By generating adverse event-related disproportionality signals for various therapies and therapeutic classes used in AD and PsO, several potential mechanisms for the AD-PsO phenotypic switch were uncovered. This includes mechanisms involving T H 2 and T H 22 repolarization, T H 17 and T H 22 repolarization, and immune shifting between T H 1, T H 17, and T H 2 cells. Clinically and immunologically related conditions were also analyzed to gain a clearer understanding of the specificity of the switch from PsO to an eczematous phenotype. Together, these findings provide mechanistic insight into the underpinnings behind the AD-PsO phenotypic switch through a novel approach, adding evidence to the fluid nature of immune phenotypes in common medical conditions. One Sentence Summary The atopic dermatitis-psoriasis phenotypic switch creates an overlap phenotype that is likely T H 22-driven.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".