Impaired decision-making in symptomatic anorexia and bulimia nervosa patients: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Impaired decision-making is a potential neurocognitive phenotype of eating disorders. It is therefore important to disentangle the decision-making deficits associated with the eating disorder subtypes and determine whether this putative impairment is a state or trait marker of the disease or more related to starvation. We systematically reviewed the literature on decision-making in eating disorders and conducted a meta-analysis to explore its role in anorexia nervosa (AN), bulimia nervosa (BN) and binge-eating disorder (BED). METHOD: A search of the Medline and EMBASE databases and article references was performed. A total of 23 studies (2044 participants) met the selection criteria. When the Iowa gambling task (IGT) was used in at least three of the studies, a meta-analysis was run. RESULTS: IGT performance was significantly worse in patients with an eating disorder diagnosis (AN, BN or BED) compared with healthy controls, indicating that eating disorders have a negative effect on decision-making. Hedges' g effect sizes were moderate to large (-0.72 in AN, -0.62 in BN, and -1.26 in BED). Recovered AN patients had IGT scores similar to those of healthy controls. Restrictive AN patients had significantly lower IGT net scores than purging AN patients, and both AN subtypes had worse performances than healthy controls. Age and body mass index did not explain results. CONCLUSIONS: Decision-making was significantly altered in patients with eating disorders. Poor decision-making was more pronounced during the acute phase than in the recovered state of AN. Nutritional status during the acute phase of the disease did not seem to influence decision-making skills.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.039 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".