Addressing the EDNOS issue and improving upon the utility of DSM‐IV: Classifying eating disorders using symptom profiles
Bibliographic record
Abstract
OBJECTIVE: To compare the descriptive and clinical utility of two classification systems: DSM-IV eating disorder diagnoses and proposed symptom profiles. The symptom profiles are based on the presence of overvalued ideas about shape/weight, as well as combinations of three key eating disorder symptoms (e.g. body mass index (BMI) above or below threshold and the presence or absence of bingeing and purging behaviours). METHOD: The two systems were compared on their ability to offer descriptively useful information in classifying individuals with eating disorders. In addition, we examined our system's unique contribution to clinical outcome and its relation to readiness for change. RESULTS: Classifying individuals via symptom profiles provided information about eating disorder not otherwise specified (EDNOS), a prevalent, heterogeneous and under-researched diagnostic category. Symptom profiles outperformed the DSM-IV diagnostic system in the ability to account for variation in patients' decision to enrol in treatment, performing comparably to readiness for change. CONCLUSION: Classifying individuals according to symptom profile and readiness for change appears to have more descriptive and clinical utility than the current diagnostic system.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".