A review of medication use for children and adolescents with eating disorders.
Bibliographic record
Abstract
OBJECTIVE: This paper aims to review the research literature on the use of medication for eating disorders in children and adolescents. METHOD: The literature was reviewed on the pharmacotherapy of anorexia nervosa (AN), bulimia nervosa (BN) and eating disorder not otherwise specified (EDNOS). The PubMed database was searched for all articles on medication use in the child and adolescent population using the terms medication, antipsychotic, antidepressant, child, adolescent, eating disorders, anorexia nervosa and bulimia nervosa. RESULTS: Very little literature exists on the use of medication for the treatment of eating disorders in children and adolescents. There is one retrospective study on the use of SSRIs and some case reports on atypical antipsychotics for children and adolescents with AN, and one small open trial on SSRIs for adolescent BN. CONCLUSIONS: Evidence-based pharmacological treatment for children and adolescents with eating disorders is not yet possible due to the limited number of studies available. It appears that olanzapine and other atypical antipsychotics may prove to be promising for AN at low body weights. It remains uncertain whether SSRIs are helpful in preventing relapse in AN. For children and adolescents with BN, the first line pharmacological option is fluoxetine given the large evidence base of this drug with the adult population and a small open trial of adolescents with BN.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".