Assessing severity in anorexia nervosa: Do the DSM‐5 and an alternative severity rating based on overvaluation of weight and shape severity differ in psychological and biological correlates?
Bibliographic record
Abstract
OBJECTIVE: This study evaluated the severity ratings for anorexia nervosa (AN) in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and an alternative severity rating based on overvaluation of weight/shape, on a range of psychological and biological variables. METHOD: , SD = 2.4) were categorised using both DSM-5 severity levels (mild/moderate/severe/extreme) and weight/shape (low/high) overvaluation. The severity categories were compared on a range of psychological (e.g., eating psychopathology) and biological (e.g., sodium) variables. RESULTS: Results showed that the overvaluation of weight/shape appeared better at indexing the level of severity in psychological variables among patients with AN compared to the DSM-5 severity rating with moderate to large effect sizes. Moreover, the DSM-5 mild and moderate severity groups experienced significantly higher eating and general psychopathology than the severe and extreme groups. Finally, neither the DSM-5 nor the weight/shape severity groups differed on any of the biological variables. CONCLUSIONS: This study provided no support for the DSM-5 severity rating for AN, while initial support was found for the weight/shape overvaluation approach in indexing psychological but not biological correlates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".