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Record W4406103616 · doi:10.1177/12034754241303119

Skin Picking Disorder: A Canadian Retrospective Study of 83 Patients

2025· article· en· W4406103616 on OpenAlexaffabout
Louis Deschênes, Hélène Veillette

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2025
Typearticle
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineRetrospective cohort studyDermatologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Skin picking disorder (SPD) is classified as a primary psychodermatologic disorder, in which lesions are self-induced. It is frequently encountered by dermatologists, but the management is still a source of discomfort for the majority. OBJECTIVES: The first objective is to determine the characteristics of the SPD patients in our centre: the demographics, the psychiatric comorbidities, clinical and histopathological characteristics of SPD patients, treatments and follow-up. The second objective is to demonstrate the need for education in the dermatologic community. METHODS: Qualitative and quantitative data on SPD were collected. RESULTS: The sample comprised 83 patients, with a mean age of 62 years, and a bimodal distribution. 63.8% were female and lesions were most frequently described as excoriations (27%). Physicians observed picking from multiple body sites, the most common being upper extremities. Only 11% of patients were biopsied. Psychiatric comorbidities were frequent, especially personality traits and disorders (19.2%), and substance-related and addictive disorders (16.8%). Wide variety of treatments were prescribed, including local and supportive care. Only 41% of the sample had medical follow-ups. CONCLUSIONS: This large-scale retrospective assessment of patients diagnosed with SPD broadens the scope of a frequent disorder in dermatology, showing older age patients, unexpected psychiatric comorbidities and inadequate continuity of care. Results highlight the need for a collaborative approach and for frequent reassessments of these patients.

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 score0.037
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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