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Record W4381704884 · doi:10.5021/ad.20.270

Seborrheic Dermatitis: A Case of an Atypical Side Effect of Atypical Antipsychotics

2023· article· en· W4381704884 on OpenAlexaff
Ramy Bishay, Janis Chang, Chih‐Peng Chang

Bibliographic record

VenueAnnals of Dermatology · 2023
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsThe Scarborough HospitalUniversity of OttawaTerry Fox Research Institute
Fundersnot available
KeywordsMedicineDermatologySeborrheic dermatitis

Abstract

fetched live from OpenAlex

A 40-year-old female with depression and schizophrenia presented with a new erythematous rash with thick sebum crust on the nasolabial folds, following a change in antipsychotic medication.She had previously been treated with various antidepressants and antipsychotics, including citalopram, risperidone, and quetiapine.Due to non-adherence to oral medications, she was switched to paliperidone palmitate (INVEGA SUSTENNA ; Janssen Pharmaceuticals) injection 150 mg every four weeks.She developed several common side effects of atypical antipsychotics, including weight gain and hyperprolactinemia.Following her second dose of paliperidone, she also developed skin changes on the nasolabial folds (Fig. 1), clinically consistent with seborrheic dermatitis (SD).She did not have known SD previously.There were no other medication changes prior to this presentation.The patient's SD did not improve significantly with topical therapy-first 2% ketoconazole with 1% hydrocortisone cream, which was subsequently changed to 2% ketoconazole with 1% salicylic acid and 3% sulfur.The patient's antipsychotic was later switched to oral aripiprazole, a third-generation antipsychotic, and her skin condition progressively resolved (Fig. 2).SD affects 1% to 3% of the general population 1,2 .While other factors including genetic predisposition and lifestyle likely

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.391
Teacher spread0.353 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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