The prevalence of body dysmorphic disorder in outpatient dermatology clinics: a systematic review
Bibliographic record
Abstract
BACKGROUND: Body dysmorphic disorder (BDD) is a psychiatric condition involving a preoccupation with physical appearance disproportionate to physical findings, which are often absent altogether. Previously published data have estimated its prevalence at approximately 11.3-11.9%, across various medical specialties. No recent systematic reviews strictly related to dermatology clinics and the prevalence of BDD have been published to the best of our knowledge. OBJECTIVES: To gather a pooled prevalence for BDD in outpatient dermatology clinics around the world. METHODS: A literature review was completed as per PRISMA guidelines. Studies had to meet the following inclusion criteria: (i) observational studies (either prospective, cross-sectional or case-controlled), (ii) include patients aged ≥ 13 years of age; (iii) undertaken in dermatology clinics (medical, cosmetic or specialty clinics); (iv) use validated, or translations of validated screening tools, used for BDD [such as the Body Dysmorphic Disorder Questionnaire (BDDQ), the Structured Clinical Interview for DSM-5, BDDQ-Dermatology Version (DV), the Yale-Brown Obsessive-Compulsive Scale modified for Body Dysmorphic Disorder]; (v) report a point prevalence for BDD; and (vi) be written in either English, French or Arabic, the three languages fluently spoken by the Lebanese researchers. RESULTS: Twenty-one articles tackling BDD in outpatient cosmetic and general dermatology clinics were selected. Studies were graded based on the Newcastle-Ottawa Scale and a mean for the pooled prevalence was calculated, yielding a weighted mean prevalence of 12.5% among general dermatology patients (n = 1082) and 25.0% among cosmetic dermatology patients (n = 218). The mean prevalence of BDD among general dermatology patients is similar to previously reported numbers. For cosmetic dermatology patients, the findings are markedly higher than previously reported, which we postulate could be owing to dermatologists being at the forefront of noninvasive cosmetic procedures. CONCLUSIONS: Given the high prevalence of BDD among dermatology patients, we highlight the importance of having a high index of suspicion for BDD and identifying it in the clinical setting and also emphasize the importance of psychiatric treatment for these patients to improve outcomes, all while avoiding unnecessary interventions.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".