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Record W4390176123 · doi:10.3390/pharmaceutics16010023

Phosphodiesterase-4 Inhibition in the Management of Psoriasis

2023· review· en· W4390176123 on OpenAlexaff
Erika L. Crowley, Melinda Gooderham

Bibliographic record

VenuePharmaceutics · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsUniversity of British ColumbiaSKiN HealthQueen's UniversityOkanagan University CollegeProbity Medical ResearchUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsApremilastPsoriasisMedicineRoflumilastAdverse effectClinical trialDermatologyAtopic dermatitisPhosphodiesteraseIntensive care medicinePharmacologyInternal medicinePsoriatic arthritisPulmonary disease

Abstract

fetched live from OpenAlex

Psoriasis is a common chronic immune-mediated disease with many comorbidities and impacts on quality of life. Among the treatments for psoriasis, phosphodiesterase-4 (PDE4) inhibitors are emerging with expanding options. PDE4 inhibitors play a pivotal role in the inflammatory cascade by degrading cyclic adenosine monophosphate (cAMP), contributing to pro-inflammatory mediator production. Apremilast, an oral PDE4 inhibitor, is approved for psoriasis. While effective, its adverse effects can limit its utility. Roflumilast, a topical PDE4 inhibitor, was also recently approved for psoriasis and shows promise in clinical trials. Crisaborole, a PDE4 inhibitor approved for atopic dermatitis, has also been studied in psoriasis. This review summarizes evidence from randomized clinical trials regarding the efficacy and safety of PDE4 inhibitors in psoriasis treatment. By highlighting their potential benefits and limitations, this review provides valuable insights for clinicians and researchers aiming to optimize psoriasis management.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.154
GPT teacher head0.388
Teacher spread0.235 · 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

Citations36
Published2023
Admission routes1
Has abstractyes

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