Screening for<i>DSM-5</i>Other Specified Feeding or Eating Disorder in a Weight-Loss Treatment–Seeking Obese Sample
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of specific self-report questionnaires in detecting DSM-5 eating disorders identified via structured clinical interview in a weight-loss treatment-seeking obese sample, to improve eating disorder recognition in general clinical settings. METHOD: Individuals were recruited over a 3-month period (November 2, 2011, to January 10, 2012) when initially presenting to a hospital-based weight-management center in the northeastern United States, which offers evaluation and treatment for outpatients who are overweight or obese. Participants (N = 100) completed the Structured Clinical Interview for DSM-IV eating disorder module, a DSM-5 feeding and eating disorders interview, and a battery of self-report questionnaires. RESULTS: Self-reports and interviews agreed substantially in the identification of bulimia nervosa (DSM-IV and DSM-5: tau-b = 0.71, P < .001) and binge-eating disorder (DSM-IV and DSM-5: tau-b = 0.60, P < .001), modestly for subthreshold binge-eating disorder (tau-b = 0.44, P < .001), and poorly for other subthreshold conditions (night-eating syndrome: tau-b = -0.04, P = .72, r = 0.06 [DSM-5]). DISCUSSION: Current self-report assessments are likely to identify full syndrome DSM-5 eating disorders in treatment-seeking obese samples, but unlikely to detect DSM-5 other specified feeding or eating disorders. We propose specific content changes that might enhance clinical utility as suggestions for future evaluation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".