Introducing Diagnostic Classification Modeling as an Unsupervised Method for Screening Probable Eating Disorders
Bibliographic record
Abstract
Screening for eating disorders (EDs) is an essential part of the prevention and intervention of EDs. Traditional screening methods mostly rely on predefined cutoff scores which have limitations of generalizability and may produce biased results when the cutoff scores are used in populations where the instruments or cutoff scores have not been validated. Compared to the traditional cutoff score approach, the diagnostic classification modeling (DCM) approach can provide psychometric and classification information simultaneously and has been used for diagnosing mental disorders. In the present study, we introduce DCM as an innovative and alternative approach to screening individuals at risk of EDs. To illustrate the practical utility of DCM, we provide two examples: one involving the application of DCM to examine probable ED status from the 12-item Short form of the Eating Disorder Examination-Questionnaire (EDE-QS) to screen probable thinness-oriented EDs and the Muscularity-Oriented Eating Test (MOET) to screen probable muscularity-oriented EDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".