Body dysmorphic disorder: a treatment synthesis and consensus on behalf of the International College of Obsessive-Compulsive Spectrum Disorders and the Obsessive Compulsive and Related Disorders Network of the European College of Neuropsychopharmacology
Bibliographic record
Abstract
Body dysmorphic disorder (BDD) is characterized by a preoccupation with a perceived appearance flaw or flaws that are not observable to others. BDD is associated with distress and impairment of functioning. Psychiatric comorbidities, including depression, social anxiety, and obsessive-compulsive disorder are common and impact treatment. Treatment should encompass psychoeducation, particularly addressing the dangers associated with cosmetic procedures, and may require high doses of selective serotonin reuptake inhibitors* (SSRI*) and protracted periods to establish full benefit. If there is an inadequate response to SSRIs, various adjunctive medications can be employed including atypical antipsychotics*, anxiolytics*, and the anticonvulsant levetiracetam*. However, large-scale randomized controlled trials are lacking and BDD is not an approved indication for these medications. Oxytocin* may have a potential role in treating BDD, but this requires further exploration. Cognitive-behavioural therapy has good evidence for efficacy for BDD, and on-line and telephone-assisted forms of therapy are showing promise. CBT for BDD should be customized to address such issues as mirror use, perturbations of gaze, and misinterpretation of others' emotions, as well as overvalued ideas about how others view the individual.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".