Skin Picking Disorder: A Canadian Retrospective Study of 83 Patients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".