Specific depressive symptoms predict remission to aripiprazole augmentation in late‐life treatment resistant depression
Bibliographic record
Abstract
OBJECTIVE: To identify which specific depressive symptoms predict remission to aripiprazole augmentation in late-life treatment resistant depression. METHODS: This is a secondary analysis of data from a late-life treatment resistant depression trial examining the safety and efficacy of aripiprazole augmentation. Participants aged 60 and above were randomized to aripiprazole augmentation (N = 91) versus placebo (N = 90). The main outcome was depression remission. Clinical predictors included individual Montgomery-Asberg Depression Rating Scale (MADRS) item scores categorized as symptomatic (scores >2) or nonsymptomatic (scores ≤2). RESULTS: Three MADRS items predicted depression remission with aripiprazole augmentation: symptomatic scores on sleep disturbance and nonsymptomatic scores on apparent sadness and inability to feel. The 2-way and 3-way interaction terms of these MADRS items were not significant predictors of remission; therefore, the models' ability to predict remission was not improved by combining the significant MADRS items. CONCLUSIONS: The identification of specific depressive symptoms, which can be clinically assessed, can be used to inform treatment decisions. Older adults with treatment resistant depression that present with sleep disturbances, lack of apparent sadness, or lack of inability to feel should be considered for aripiprazole augmentation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".