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Record W3138450947 · doi:10.2147/ptt.s303634

Phosphodiesterase-4 Inhibition in Psoriasis

2021· review· en· W3138450947 on OpenAlexaff
Milica Milakovic, Melinda Gooderham

Bibliographic record

VenuePsoriasis Targets and Therapy · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsProbity Medical ResearchSKiN HealthQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsApremilastPsoriasisRoflumilastMedicinePhosphodiesterasePharmacologyAdenosine monophosphatePhosphodiesterase inhibitorAdverse effectAdenosineAtopic dermatitisCyclic adenosine monophosphateDermatologyInternal medicineEnzymeChemistryPsoriatic arthritisBiochemistry

Abstract

fetched live from OpenAlex

Psoriasis is a chronic immune-mediated inflammatory disorder. Phosphodiesterase-4 (PDE-4) is an enzyme that mediates inflammatory responses and plays a role in psoriasis pathogenesis. PDE-4 degrades its substrate cyclic adenosine monophosphate (cAMP) to adenosine monophosphate (AMP), which subsequently leads to the production of pro-inflammatory mediators. Inhibitors of PDE-4 work by blocking the degradation of cAMP, which leads to a reduction in inflammation. Apremilast is the only approved oral PDE-4 inhibitor for the treatment of psoriasis. While it is effective for some patients, it may be limited by adverse effects in others. A topical PDE-4 inhibitor, roflumilast, is being investigated in psoriasis and showing promising results. Crisaborole, a topical PDE-4 inhibitor approved for use in atopic dermatitis, has also been investigated in psoriasis. This is an updated comprehensive review to summarize the currently available evidence for the PDE-4 inhibitors apremilast, roflumilast and crisaborole in the treatment of psoriasis, with a focus on data from randomized clinical trials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.039
GPT teacher head0.292
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations81
Published2021
Admission routes1
Has abstractyes

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