Inference‐Based Therapy for Body Dysmorphic Disorder
Bibliographic record
Abstract
Body dysmorphic disorder (BDD) is a debilitating disorder characterized by an excessive pre-occupation with an imagined or very slight defect in one's physical appearance. Despite the overall success of cognitive behavioural therapy (CBT) in treating BDD, some people do not seem to benefit as much from this approach. Those with high overvalued ideation (OVI), for instance, have been shown to not respond well with CBT. The purpose of this study was to evaluate the efficacy of an inference-based therapy (IBT) in treating BDD. IBT is a cognitive intervention that was first developed for obsessive-compulsive disorder with high OVI, but whose focus on beliefs can also apply to a BDD population. IBT conceptualizes BDD obsessions (e.g., 'I feel like my head is deformed') as idiosyncratic inferences arrived at through inductive reasoning processes. Such primary inferences represent the starting point of obsessional doubt and the treatment focuses on addressing the faulty inferences that maintain the doubt. Thirteen BDD participants, of whom 10 completed, underwent a 20-week IBT for BDD. The participants improved significantly over the course of therapy, with large diminutions in BDD and depressive symptoms. OVI also decreased throughout therapy and was not found to be related to reduction in BDD symptoms. Although a controlled-trial comparing CBT with IBT is needed, it is proposed that IBT constitutes a promising treatment alternative for BDD especially in cases where OVI is high.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".