Acute and chronic stress predict anti-depressant treatment outcome and naturalistic course of major depression: A CAN-BIND report
Bibliographic record
Abstract
BACKGROUND: In treatment studies of major depressive disorder (MDD), exposure to major life events predicts less symptom improvement and greater likelihood of relapse. In contrast, the impact of minor life events has received less attention. We hypothesized that the impact of minor events on symptom improvement and risk of relapse would be heightened in the presence of concurrent chronic stress. We also hypothesized that major events would predict less symptom improvement and greater risk of relapse independently of chronic stress. METHODS: Adult patients experiencing an episode of MDD were enrolled into a 16-week trial with antidepressant treatments (n = 156). Forty-three fully remitted patients agreed to participate in a naturalistic 18-month follow-up, and 30 had full data for analyses. Life events and chronic stressors were assessed using a contextual life stress interview. RESULTS: Greater exposure to minor events predicted greater improvement in symptoms during acute treatment, but this relation was specific to those who reported greater severity of chronic stress. During follow-up, however, major life events predicted increased risk of relapse, and this effect was not moderated by chronic stress. LIMITATION: High attrition rates led to a small sample size for the follow-up analyses. CONCLUSIONS: Exposure to minor events may provide an opportunity to practice problem-solving skills, thereby facilitating symptom improvement. Nevertheless, acute treatment did not protect patients from relapse when they subsequently faced major events during follow-up. Therefore, adjunctive strategies may be needed to enhance outcomes during pharmacotherapy, consolidating benefits from acute treatment and providing skills to prevent relapse.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".