Fluoxetine for anorexia nervosa after weight restoration: moderation of effect by depression
Bibliographic record
Abstract
Abstract Background Pharmacological efforts to treat anorexia nervosa (AN) have predominantly repurposed medications that treat conditions with overlapping symptoms and yielded generally disappointing results. Despite limited empirical support, SSRIs are often prescribed to patients with AN. Whether SSRIs are effective in a subgroup of individuals with AN, such as those with depression, is not known. Methods A secondary analysis of a randomized trial of fluoxetine versus placebo for relapse prevention in AN was conducted. Participants ( n = 92) were weight-restored women with AN who completed the Beck Depression Inventory (BDI) at the time of randomization. BDI scores were dichotomized to reflect moderate/severe depression (BDI > 20, n = 26). A Cox Proportional Hazards model estimated the association of the level of depression, medication, and their interaction with time to relapse. Mixed effects models examined the effects of medication on symptom trajectories in high versus low depression groups and whether depression severity modified the effect of the drug on symptom trajectory. Results There was a significant interaction between medication and depression severity in time to relapse (hazard ratio = 0.46, 95% CI: [0.25, 0.85], p = .01). Depression severity modified the effect of fluoxetine on the time course of symptoms of depression ( β = −0.27, 95% CI: [−0.42,-0.12], p = 0.001) and bulimia ( β = −0.15, 95% CI: [−0.25,-0.05], p = 0.004) in the twelve month follow-up period. Conclusions Fluoxetine was more effective than placebo in reducing relapse among more depressed, weight-restored individuals with AN. These results require replication but provide support for the use of antidepressant medication for patients with AN who remain depressed following weight restoration.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".