LATENT CLASSES OF NONRESPONDERS, RAPID RESPONDERS, AND GRADUAL RESPONDERS IN DEPRESSED OUTPATIENTS RECEIVING ANTIDEPRESSANT MEDICATION AND PSYCHOTHERAPY
Bibliographic record
Abstract
BACKGROUND: We used growth mixture modeling (GMM) to identify subsets of patients with qualitatively distinct symptom trajectories resulting from treatment. Existing studies have focused on 12-week antidepressant trials. We used data from a concurrent antidepressant and psychotherapy trial over a 6-month period. METHOD: Eight hundred twenty-one patients were randomized to receive either fluoxetine or tianepine and received cognitive-behavioral therapy, supportive therapy, or psychodynamic therapy. Patients completed the Montgomery-Åsberg depression rating scale (MADRS) at the 0, 1, 3, and 6-month periods. Patients also completed measures of dysfunctional attitudes, functioning, and personality. GMM was conducted using MADRS scores and the number of growth classes to be retained was based on the Bayesian information criterion. RESULTS: Criteria supported the presence of four distinct latent growth classes representing gradual responders of high severity (42% of sample), gradual responders of moderate severity (31%), nonresponders (15%), and rapid responders (11%). Initial severity, greater use of emotional coping strategies, less use of avoidance coping strategies, introversion, and less emotional stability predicted nonresponder status. Growth classes were not associated with different treatments or with proportion of dropouts. CONCLUSIONS: The longer time period used in this study highlights potential overestimates of nonresponders in previous research and the need for continued assessments. Our findings demonstrate distinct growth trajectories that are independent of treatment modality and generalizable to most psychotherapy patients. The correlates of class membership provide directions for future studies, which can refine methods to predict likely nonresponders as a means to facilitate personalized 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".