Melancholic and atypical depression as predictor and moderator of outcome in cognitive behavior therapy and pharmacotherapy for adult depression
Bibliographic record
Abstract
BACKGROUND: Melancholic and atypical depression are widely thought to moderate or predict outcome of pharmacological and psychological treatments of adult depression, but that has not yet been established. This study uses the data from four earlier trials comparing cognitive behavior therapy (CBT) versus antidepressant medications (ADMs; and pill placebo when available) to examine the extent to which melancholic and atypical depression moderate or predict outcome in an "individual patient data" meta-analysis. METHODS: We conducted a systematic search for studies directly comparing CBT versus ADM, contacted the researchers, integrated the resulting datasets from these studies into one big dataset, and selected the studies that included melancholic or atypical depressive subtyping according to DSM-IV criteria at baseline (n = 4, with 805 patients). After multiple imputation of missing data at posttest, mixed models were used to conduct the main analyses. RESULTS: In none of the analyses was melancholic or atypical depression found to significantly moderate outcome (indicating a better or worse outcome of these patients in CBT compared to ADM; i.e., an interaction), predict outcome independent of treatment group (i.e., a main effect), or predict outcome within a given modality. The outcome differences between patients with melancholia or atypical depression versus those without were consistently very small (all effect sizes g < 0.10). CONCLUSIONS: We found no indication that melancholic or atypical depressions are significant or relevant moderators or predictors of outcome of CBT and ADM.
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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 | 0.023 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".