Early Improvement of Specific Symptoms Predicts Subsequent Recovery in Bipolar Depression
Bibliographic record
Abstract
OBJECTIVE: The aim of this post hoc analysis was to evaluate which specific depressive items could predict subsequent durable recovery in patients with bipolar depression. METHODS: The study population was at least 18 years old and met DSM-IV criteria for a major depressive episode associated with either bipolar I or II disorder. The data were derived from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD), in which patients with bipolar depression were randomly assigned to treatment for acute depression with a mood stabilizer plus an adjunctive antidepressant drug or placebo. The primary and secondary outcomes were durable recovery (ie, 8 consecutive weeks of euthymia) and treatment-emergent affective switch (ie, transition to mania or hypomania), respectively. Binary logistic regression analysis was performed to identify specific symptoms whose improvement during the first 2 weeks predicted those outcomes; the score change of each individual symptom in the continuous symptom subscales for depression (SUM-D) from week 0 to week 2 was used as an independent variable. RESULTS: In the evaluable 188 participants who took placebo and active drugs, the improvement in loss of self-esteem (P = .037) or loss of energy (P = .040) at week 2 was significantly associated with higher chances of subsequent durable recovery. For participants taking active drugs (n = 91), solely the improvement in loss of energy at week 2 was significantly associated with subsequent durable recovery (P = .027). There was a significant association between the improvement of psychomotor retardation at week 2 and subsequent affective switch (P = .008). CONCLUSIONS: These findings imply that focusing on individual symptoms is important in bipolar depression, rather than relying solely on a summed score in rating scales. TRIAL REGISTRATION: The original STEP-BD dataset is registered on ClinicalTrials.gov (identifier: NCT00012558).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".