I weigh therefore I am: Implications of using different criteria to define overvaluation of weight and shape in binge‐eating disorder
Bibliographic record
Abstract
OBJECTIVE: Research suggests that overvaluation of weight and shape is a clinical feature in binge-eating disorder (BED). However, this construct has been differentially defined in the literature even when using the same measure. Here we compare two cut-offs that have previously been used to differentiate clinical and subthreshold overvaluation using the EDE-Q. METHOD: Individuals with BED (n = 72, 93% female) and no history of an eating disorder (NED; n = 21, 91% female) completed measures of eating disorder (ED) and general psychopathology online. Individuals with BED were categorized as having clinical or subthreshold overvaluation using two different cut-offs used in previous studies. The clinical, subthreshold, and NED groups were compared on ED and general psychopathology. The association between overvaluation and psychopathology was also assessed in the BED and NED groups. RESULTS: The two cut-offs yielded identical results, with individuals in the clinical overvaluation group reporting greater ED psychopathology than those in the subthreshold and NED groups. When considered as a continuous variable, overvaluation was a significant predictor of both ED-related and general psychopathology. DISCUSSION: The two cut-offs yielded identical results, likely due to the high internal consistency between overvaluation items. Under such circumstances, the use of either cut-off seems appropriate. However, given the associations reported in the regression analyses, we propose that considering overvaluation as a dimensional variable, rather than a categorical one, may have greater utility.
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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 | 0.042 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".