Test-retest reliability of a 2-dimensional and 3-dimensional visual assessment of body image disturbance in anorexia nervosa
Bibliographic record
Abstract
Abstract Body image disturbance (BID) is a diagnostic feature of anorexia nervosa (AN), with few reliable visual perceptual or attitudinal markers. Somatomap is a 2-dimensional (2D) and 3-dimensional (3D) digital assessment of BID, which has demonstrated utility. Test-retest reliability of Somatomap 2D and 3D digital assessment of BID in AN was examined. Fifty-nine inpatient participants with AN performed test-retest by a) outlining body concern areas on a 2D avatar for each independent area of concern; and b) sculpting 23 independent body parts on a randomized 3D avatar to reflect their perceived body size in length and girth. Participants corresponding body parts were physically measured to calculate discrepancy scores (i.e., 3D perceived minus measured values). Regional 2D BID test-retest differences were evaluated using z-scores to generate statistical visual body maps. Test-retest of 3D assessment reliability was evaluated by Intraclass Correlation Coefficient for individual and aggregated body parts. Somatomap 2D demonstrated excellent test-retest reliability with no statistical differences between test and retest z-scores. All 23 body parts on Somatomap 3D demonstrated statistically significant fair-to-excellent test-retest reliability in AN. Regions that are commonly of concern in AN, and combined measures, showed the highest reliability. Results suggest that Somatomap 2D and 3D may provide a reliable perceptual marker of visual BID in AN.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".