An update on generalized pustular psoriasis
Bibliographic record
Abstract
Introduction: Generalized pustular psoriasis (GPP) is a rare, severe relapsing/remitting, multisystem disease that can be difficult to treat. Recent clinical, histological, and genetic evidence suggests that GPP is a distinct clinical entity from plaque psoriasis and requires a separate diagnosis. The interleukin-36 pathway appears to be central to GPP pathogenesis. As no therapeutic agents have been approved for GPP to date in the United States or Europe, the introduction of anti-IL-36 therapies may change disease management. Areas covered: Using PubMed and Google Scholar, we reviewed the literature for articles related to GPP, psoriasis, and the genetics, pathogenesis, and treatment thereof. Expert opinion: New therapeutic options and updated guidelines for GPP treatment are needed. Ideal agents would have rapid onset of action and rapid time to achieve disease clearance, have the ability to prevent acute flares and avert recurrence, and possess a favorable safety profile. Such therapies should be readily accessible via approval or listing on formularies. Scoring systems to establish GPP disease burden and objective outcome measures could also help with further evaluation of therapies and treatment access issues. IL-36 remains a promising target, as supported by early phase data suggesting efficacy and safety for a novel anti-IL-36 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".