Which aspects of anhedonia predict response to pharmacotherapy in major depressive disorder?
Bibliographic record
Abstract
Abstract Background Anhedonia is a multidimensional concept, and it is not known which aspects of it are linked to the heterogeneity of treatment responses in major depressive disorder (MDD). We examine the role of anhedonia dimensions in predicting response to antidepressant medication and adjunctive pharmacotherapy. Methods In CAN-BIND-1, 187 adults with MDD completed the Dimensional Anhedonia Rating Scale (DARS) and the Snaith–Hamilton Pleasure Scale (SHAPS) before undergoing 8 weeks of treatment with escitalopram. At week 8, 90 nonresponders received adjunctive treatment with aripiprazole for an additional 8 weeks. Mixed-effects models tested the hobbies, food, social, and sensory subscales and items of DARS and SHAPS as predictors of change in the Montgomery-Åsberg Depression Rating Scale (MADRS). Results Of the four DARS subscales, sensory anhedonia predicted a worse treatment outcome with escitalopram ( b = 1.14, 95%CI 0.08 to 2.20, p = 0.034) as did a three-item SHAPS sensory anhedonia subscale ( b = 1.50, 95%CI 0.43 to 2.57, p = 0.006). A combined DARS–SHAPS sensory anhedonia subscale complemented the previously reported interest–activity symptom dimension to improve treatment outcome prediction. In contrast, food and social anhedonia dimensions predicted worse outcomes with adjunctive aripiprazole ( b = 2.52, 95%CI 1.25 to 3.80, p < 0.001; b = 2.56, 95%CI 1.16 to 3.96, p < 0.001). Corresponding SHAPS items showed similar results. Conclusions The inability to enjoy sensory experiences and the lack of interest in food and social activities distinctly predict outcomes with serotonergic versus dopaminergic pharmacotherapy. These findings require replication and extension to other treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".