Do supplementary items on the eating disorder examination improve the assessment of adolescents with anorexia nervosa?
Bibliographic record
Abstract
OBJECTIVE: Given that adolescents with anorexia nervosa (AN) typically have lower scores on the Eating Disorder Examination (EDE) than expected, the current study examined whether the inclusion of eight supplementary items developed by the authors of the EDE better captured the symptoms of adolescents with AN. METHOD: A dataset consisting of EDEs from 86 adolescents was examined by 3 primary methods: (1) baseline subscale scores were compared before and after the addition of the supplementary items, (2) the internal consistency of the EDE with the addition of these items was examined, and (3) each of these items was compared before and after treatment. RESULTS: After the addition of the supplementary items, the Eating Concern and Weight Concern subscales were significantly increased, whereas the Restraint subscale was significantly decreased, and the Shape Concern subscale was unchanged. Internal consistency was improved on the Eating Concern, Weight Concern, and Shape Concern subscales, and was decreased on the Restraint subscale. Three of eight items showed a significant decrease with treatment. CONCLUSION: Although the addition of some of these eight supplementary items better captured the psychopathology of adolescents with AN, scores were still substantially below expected, indicating that the exploration of other methods of assessment is needed.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".