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Record W2781701639 · doi:10.1177/1203475417748886

Management of Common Side Effects of Apremilast

2017· review· en· W2781701639 on OpenAlexaff
Annie Langley, Jennifer Beecker

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2017
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineApremilastDiscontinuationNauseaSide effect (computer science)PsoriasisIntensive care medicineDiarrheaThalidomideDermatologyPsychological interventionSurgeryInternal medicinePsoriatic arthritisPsychiatry

Abstract

fetched live from OpenAlex

Apremilast is a relatively new therapy for the treatment of moderate to severe plaque psoriasis in adults. While this medication is considered safe with a very low risk of serious side effects, a few common (≥5% of patients) mild to moderate side effects have been reported, including diarrhea, nausea, headache, and nasopharyngitis. Not addressing these symptoms may lead to medication nonadherence and unnecessary discontinuation of therapy. These side effects are often easily managed with interventions available to the practicing dermatologist, and in only rare instances will these side effects require dose adjustment or discontinuation of therapy. The purpose of this article is to review common side effects of apremilast at its approved dose of 30 mg orally twice daily (BID) and to provide clear, simple recommendations for their management in dermatological practice.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.054
GPT teacher head0.316
Teacher spread0.262 · 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
Published2017
Admission routes1
Has abstractyes

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