Quantifying body size estimation accuracy and body dissatisfaction in body dysmorphic disorder using a digital avatar
Bibliographic record
Abstract
Abstract A core feature of body dysmorphic disorder (BDD) is body image disturbance. Many with BDD misperceive and are dissatisfied with the sizes and shapes of body parts, but detailed quantification and analysis of this has not yet been performed. To address this gap, we applied Somatomap 3D, a digital avatar tool, to quantify body image disturbances by assessing body size estimation (BSE) accuracy and body dissatisfaction. Sixty-one adults (31 with BDD, 30 healthy controls) created avatars to reflect their perceived current body and ideal body by altering 23 body part sizes and lengths using Somatomap 3D. Physical measurements of corresponding body parts were recorded for comparison. BSE accuracy (current minus actual) and body dissatisfaction (ideal minus current) were compared between groups and in relation to BDD symptom severity using generalized estimating equations. Individuals with BDD significantly over-and under-estimated certain body parts compared to healthy controls. Individuals with BDD overall desired significantly thinner body parts compared to healthy controls. Moreover, those with worse BSE accuracy had greater body dissatisfaction and poorer insight. In sum, this digital avatar tool revealed disturbances in body image in individuals with BDD that may have perceptual and cognitive/affective components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".