Pharmacological Interventions for Primary Psychodermatologic Disorders: An Evidence Mapping and Appraisal of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: The lack of clinical guidelines for the treatment of primary psychodermatologic disorders (PPDs) hinders the delivery of optimal care to patients. The review aimed to identify, appraise, and summarize the currently available evidence about the safety and effectiveness of pharmacological management of PPDs through randomized controlled trials (RCTs). METHODS: The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRIMSA) statement and the Global Evidence Mapping Initiative guidance were followed. Medline, Embase, PsycInfo, Cochrane and Scopus were searched, and two reviewers independently completed article review, data extraction, and quality assessment. RESULTS: = 1). Seven different classes of medications were investigated: SSRIs (i.e., fluoxetine, sertraline, and citalopram), tricyclic antidepressants (i.e., clomipramine and desipramine), antipsychotics (i.e., olanzapine and pimozide), anticonvulsant (i.e., lamotrigine), N-acetylcysteine, inositol, and milk thistle. RCT-derived evidence supports the use of antidepressants in trichotillomania (sertraline and clomipramine), pathologic skin picking (fluoxetine), pathologic nail biting and dermatitis from compulsive hand washing (clomipramine or desipramine); antipsychotics in trichotillomania (olanzapine) and delusional parasitosis (pimozide); N-acetyl cysteine in trichotillomania and skin picking. CONCLUSION: Few pharmacotherapies for primary psychodermatologic disorders are assessed through controlled trials in the literature. This review serves as a roadmap for researchers and clinicians to reach informed decisions with current evidence, and to build on it to establish guidelines in the future.
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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 | 0.093 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".