Treatment strategies for skin picking disorder: Efficacy of pharmacological and nonpharmacological approaches—A systematic review and evidence mapping
Bibliographic record
Abstract
Skin picking disorder (SPD), classified in the DSM-5 under Obsessive-Compulsive and Related Disorders, is characterized by repetitive skin picking that leads to tissue damage. Despite its prevalence, there are currently no Food and Drug Administration-approved treatments. This systematic review aimed to assess both pharmacological and non-pharmacological interventions for SPD. A comprehensive literature search identified 25 randomized controlled trials comprising 3,407 participants (mean age 29.6 years; predominantly women). Treatments included pharmacological agents, cognitive-behavioral therapies, behavioral modification techniques, self-help tools, and neurostimulation. Outcome measures were primarily derived from validated scales such as the Skin Picking Scale, Skin Picking Impact Scale, and the Yale-Brown Obsessive Compulsive Scale. Pharmacological treatments averaged a 49.1% reduction in symptoms, with fluoxetine achieving a notably high (98.3%) reduction in patients with acne excoriée. Other promising agents included memantine, dronabinol, and N-acetylcysteine. Cognitive and behavioral therapies achieved a 39.8% average reduction, with cognitive behavioral therapy alone reducing symptoms by 51%. Behavioral modification approaches such as habit reversal training demonstrated a 45% reduction. Self-help tools and neurostimulation therapies showed moderate efficacy.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".