Does food addiction distinguish a specific subgroup of overweight/obese overeating women?
Bibliographic record
Abstract
Neurophysiological and behavioral similarities have been evidenced between excessive food consumption leading to obesity and addiction to other substances. In accordance, food addiction was defined following the DSM-IV diagnostic criteria for substance dependence. The aim of this pilot study was to identify a subgroup of women suffering from food addiction (n = 11), and to compare them to women suffering from substance-use disorder (n = 23), and to women seeking treatment for compulsive overeating but free from food addiction (n = 12) on addiction-related characteristics (reward sensitivity, impulsivity, personality traits, depression, emotion dysregulation). We hypothesized that women with food addiction would be similar to women with substance-use disorders, and different from women with compulsive overeating without food addiction. Participants completed self-reported questionnaires assessing food addiction and other variables related to addiction. Almost half (47.8%) of women with compulsive overeating fulfilled the criteria for food addiction. Although food addiction does not account for every case of compulsive overeating, it characterizes a specific subgroup of overweight/obese women who show more severe overeating. Women with food addiction seem to be more similar to women suffering from substance-use disorders than to other women with overeating difficulties, particularly regarding impulsivity and self-directedness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".