Eating Disorders: Anorexia Nervosa, Bulimia Nervosa, and Binge Eating Disorder
Bibliographic record
Abstract
This chapter addresses anorexia nervosa, bulimia nervosa, and the various eating syndromes that are classified as eating disorders not otherwise specified, providing an up-to-date discussion on phenomenology (including eating-specific and comorbid features), epidemiology, and etiology. In the eating disorders, interactive effects involving environmental risks (e.g., cultural inducements toward excessive dieting or overachievement) and constitutional susceptibilities in vulnerable individuals (e.g., heritable propensities toward appetitive dysregulation, affective instability, or excessive anxiety) are highly evident, perhaps more so than in many other mental-health problems. As a result, the eating disorders encourage us to think in terms of multiple determinants and maintaining factors. A main organizing concept throughout this chapter is a multidimensional etiological concept that integrates findings from biological, psychological, family-developmental, and cross-cultural studies and, more importantly, from studies on constitution x environment interactions. The chapter also provides a heuristic for differentiating factors that constitute disorder-specific risks from those that contribute (and that may explain comorbid traits, like marked impulsivity or affective instability) without having specific causal roles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".