Implications of DSM‐5 for the diagnosis of pediatric eating disorders
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to compare the DSM-IV, DSM-5, and ICD-10 eating disorders (ED) nomenclatures to assess their value in the classification of pediatric eating disorders. We investigated the prevalence of the disorders in accordance with each system's diagnostic criteria, diagnostic concordance between the systems, and interrater reliability. METHOD: Participants were 1062 children and adolescents assessed at intake to a specialist Eating Disorders Program (91.6% female, mean age 14.5 years, SD = 1.75). Measures were collected from routine intake assessments. RESULTS: DSM-5 categorization led to a lower prevalence of unspecified EDs when compared with DSM-IV. There was almost complete overlap for specified EDs. Kappa values indicated almost excellent agreement between the two coders on all three diagnostic systems, although there was higher interrater reliability for DSM-5 and ICD-10 when compared with DSM-IV. DISCUSSION: DSM-5 nomenclature is useful in classifying eating disorders in pediatric clinical samples.
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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 | 0.030 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".