Follow-up of monotherapy remitters in the PReDICT study: Maintenance treatment outcomes and clinical predictors of recurrence.
Bibliographic record
Abstract
OBJECTIVE: This study followed remitted patients from a randomized controlled trial of adults with major depressive disorder (MDD). The aims were to describe rates of recurrence and to evaluate 3 clinical predictor domains. METHOD: Ninety-four treatment-naïve patients (50% female; Mage = 38.1 years; 48.9% White; 30.9% Hispanic) with MDD who had remitted to 12-week monotherapy (escitalopram, duloxetine, or cognitive behavior therapy [CBT]) participated in a 21-month maintenance phase (i.e., continued medication or 3 possible CBT booster sessions per year). Recurrence was assessed quarterly, and the clinical predictors were the following: 2 measures of residual depressive symptoms, 1 measure of lifetime depressive episodes, and 2 measures of baseline anxiety. Survival analysis models evaluated recurrence rates, and regression models evaluated the predictors. RESULTS: Among all patients, 15.5% experienced a recurrence, and the survival distributions did not statistically differ among treatments. Residual depressive symptoms on the Hamilton Depression Rating Scale at the end of monotherapy were associated with increased risk for recurrence (hazard ratio = 1.31, 95% confidence interval [CI: 1.02, 1.67], Wald χ2 = 4.41, p = .036), and not having a comorbid anxiety disorder diagnosis at study baseline reduced the risk of recurrence (hazard ratio = .31, 95% CI [.10, .94], Wald χ2 = 4.28, p = .039). CONCLUSIONS: The study supported the benefits of maintenance treatment for treatment-naïve patients who remitted to initial monotherapy; nevertheless, remitted patients with a comorbid anxiety disorder diagnosis at the beginning of treatment or residual depressive symptoms after initial treatment were at risk for poorer long-term outcomes. (PsycINFO Database Record
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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 | 0.003 |
| 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 | 0.001 | 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".