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Record W2971952931 · doi:10.1177/1203475419857668

Off-Label Use of Topical Calcineurin Inhibitors in Dermatologic Disorders

2019· review· en· W2971952931 on OpenAlexaff
Lyn Guenther, Charles Lynde, Yves Poulin

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsCentre de Recherche Dermatologique du Québec MétropolitainUniversité LavalProbity Medical ResearchUniversity of TorontoWestern University
FundersLEO PharmaLEO Pharma Research FoundationCelgeneEli Lilly and Company
KeywordsMedicinePimecrolimusDermatologyCalcineurinTacrolimusSeborrheic dermatitisVitiligoPsoriasisLichen sclerosusMorpheaAtopic dermatitisHidradenitis suppurativaDiseaseSurgeryPathologyTransplantation

Abstract

fetched live from OpenAlex

Off-label prescribing is a common practice in dermatology, particularly when uncommon dermatologic diseases have limited or no approved treatment options. Topical calcineurin inhibitors are approved for the treatment of eczema, and their anti-inflammatory, immunomodulatory, and steroid-sparing effects make them an attractive therapeutic option for a wide variety of other dermatologic diseases. This review summarizes and qualifies the available evidence supporting the clinical effectiveness of tacrolimus ointment and pimecrolimus cream in non-eczema indications. There is high-quality evidence supporting the effectiveness of topical calcineurin inhibitors in multiple dermatological disorders including vitiligo; psoriasis of the face, folds, and genitals; seborrheic dermatitis; chronic hand dermatitis; contact dermatitis; oral lichen planus; lichen sclerosus; morphea; and cutaneous lupus erythematosus. Lower-quality evidence suggests they may be considered as an option in many other cutaneous disorders.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.095
GPT teacher head0.349
Teacher spread0.254 · 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

Citations48
Published2019
Admission routes1
Has abstractyes

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