Bibliographic record
Abstract
Eating disorders and drug addiction share many common traits. This includes biological and environmental factors that predispose individuals to develop either disorder, an increased risk for anxiety and depression when the disorders are present, and heightened trait levels of impulsivity and compulsion. Animal models of eating disorders are not as well established as those that model drug addiction, but the research in this area is progressing rapidly. In this chapter, we discuss anorexia nervosa, bulimia nervosa, binge eating disorder, and obesity as these encompass the majority of maladaptive eating behaviors in humans. We begin by outlining the important features that characterize each disorder and that should thereby be present in an animal model. An overview of peptide control of feeding is provided to help the reader evaluate the animal models presented. These are based principally on genetic variation and stressful life events. In general, most animal models based on genetic alterations have limited applicability to humans, at least to date. Those based on stressful life events appear more promising in that they more accurately reproduce alterations in feeding and neuroendocrine function that are characteristic of each disorder. The next obvious step in eating disorder research is to combine the two approaches to determine how genetic alterations and stressful events interact to produce maladaptive eating and physiological changes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".