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Record W2972500005 · doi:10.12890/2019_001207

Linagliptin-Associated Alopecia and Bullous Pemphigoid

2019· article· en· W2972500005 on OpenAlexaff
Ali Someili, Mohannad Abu‐Hilal, Khalid Azzam

Bibliographic record

VenueEuropean Journal of Case Reports in Internal Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Bullous Skin Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLinagliptinAlopecia areataMedicineBullous pemphigoidDermatologyDipeptidyl peptidase-4 inhibitorDipeptidyl peptidase-4PemphigoidDiabetes mellitusType 2 Diabetes MellitusType 2 diabetesImmunologyEndocrinology

Abstract

fetched live from OpenAlex

Bullous pemphigoid is a chronic autoimmune blistering disease. Recently, several reports suggested dipeptidyl peptidase 4 (DPP-4) inhibitors, also known as gliptins, were a potential cause of drug-induced bullous pemphigoid but not of both bullous pemphigoid and alopecia areata together. Here we describe the case of a 68-year-old man with type 2 diabetes mellitus who developed new onset diffuse alopecia on the scalp with diffuse tense bullae over his body a few months after linagliptin was introduced for better control of his diabetes. DPP-4 inhibitors are not known to increase the risk of alopecia. To the best of our knowledge, this is the first report of linagliptin-associated alopecia areata and bullous pemphigoid, which may help demonstrate if there are any links between DPP-4 inhibitors and alopecia. LEARNING POINTS: This is the first report of linagliptin-associated alopecia areata and bullous pemphigoid (BP), which may help demonstrate a link between DPP-4 inhibitors and alopecia.Since the time of onset of BP after initiation of a DPP-4 inhibitor varies, a high index of suspicion is needed for diagnosis.Early diagnosis is essential as DPP-4 inhibitor withdrawal has a significant effect on disease remission.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.256
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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

Citations11
Published2019
Admission routes1
Has abstractyes

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