Exploring the relationship between negative urgency and dysregulated eating: Etiologic associations and the role of negative affect.
Bibliographic record
Abstract
Negative urgency (i.e., the tendency to engage in rash action in response to negative affect) has emerged as a critical personality trait contributing to individual differences in binge eating. However, studies investigating the extent to which genetic and/or environmental influences underlie the effects of negative urgency on binge eating are lacking. Moreover, it remains unclear whether negative urgency-binge eating associations are simply a result of the well-established role of negative affect in the development/maintenance of binge eating. The current study addresses these gaps by examining phenotypic and etiologic associations between negative urgency, negative affect, and dysregulated eating (i.e., binge eating, emotional eating) in a sample of 222 same-sex female twin pairs from the Michigan State University Twin Registry. Negative urgency was significantly associated with both dysregulated eating symptoms, even after controlling for the effects of negative affect. Genetic factors accounted for the majority (62-77%) of this phenotypic association, although a significant proportion of this genetic covariation was due to genetic influences in common with negative affect. Nonshared environmental factors accounted for a relatively smaller (23-38%) proportion of the association, but these nonshared environmental effects were independent of negative affect. Findings suggest that the presence of emotion-based rash action, combined with high levels of negative affect, may significantly increase genetic risk for dysregulated eating.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".