Visual mapping of body image disturbance in anorexia nervosa reveals objective markers of illness severity
Bibliographic record
Abstract
Body image disturbance (BID) is a core feature of eating disorders, for which there are few objective markers. We examined the feasibility of a novel digital tool, "Somatomap", to index BID related to anorexia nervosa (AN) severity. Fifty-five AN inpatients and 55 healthy comparisons (HC) outlined their body concerns on a 2-Dimensional avatar. Next, they indicated sizes/shapes of body parts for their current and ideal body using sliders on a 3-Dimensional avatar. Physical measurements of corresponding body parts, in cm, were collected for reference. We evaluated regional differences in BID using proportional z-scores to generate statistical body maps, and multivariate analysis of covariance to assess perceptual discrepancies for current body, ideal body, and body dissatisfaction. The AN group demonstrated greater regional perceptual inaccuracy for their current body than HC, greater discrepancies between their current and ideal body, and higher body dissatisfaction than HCs. AN body concerns localized disproportionately to the chest and lower abdomen. The number of body concerns and perceptual inaccuracy for individual body parts was strongly associated with Eating Disorder Examination Questionnaire (Global EDE-Q) scores across both groups. Somatomap demonstrated feasibility to capture multidimensional aspects of BID. Several implicit measures were significantly associated with illness severity, suggesting potential utility for identifying objective BID markers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".