Olanzapine use for the treatment of adolescents with anorexia nervosa - reflecting on research and clinical practice.
Bibliographic record
Abstract
Anorexia nervosa is a complex and potentially devastating mental health (MH) diagnosis that is recognized as having high rates of non-response to treatment, pronounced medical as well as MH morbidity, and elevated mortality rates. Olanzapine is a second-generation atypical antipsychotic that has demonstrated benefit with weight gain in adults with anorexia nervosa (AN), although controlled research involving children and youth remains limited. In this commentary, the authors provide a brief history and review of research relating to olanzapine for the adjunctive treatment of children and adolescents with AN. Although the medication has been used for more than two decades, its mechanism of action remains incompletely understood and is likely multifactorial. Despite a paucity of research to guide clinical decision making, olanzapine prescription among youth with moderate to severe AN appears to be prevalent among eating disorder specialists in Canada. In addition to commenting on gaps and challenges related to controlled randomized research in this area, the authors reflect on factors likely contributing to olanzapine's adoption into clinical practice. Moving forward, it is critical that further research involving olanzapine for the adjunctive treatment of AN in youth be undertaken to better understand efficacy, appropriate indications for use, and safety profile.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".