Treating depression to remission in older adults: a controlled evaluation of combined escitalopram with interpersonal psychotherapy <i>versus</i> escitalopram with depression care management
Bibliographic record
Abstract
OBJECTIVE: More than half of the older adults respond only partially to first-line antidepressant pharmacotherapy. Our objective was to test the hypothesis that a depression-specific psychotherapy, Interpersonal Psychotherapy (IPT), when used adjunctively with escitalopram, would lead to a higher rate of remission and faster resolution of symptoms in partial responders than escitalopram with depression care management (DCM). METHOD: We conducted a 16-week randomized clinical trial of IPT and DCM in partial responders to escitalopram, enrolling 124 outpatients aged 60 and older. The primary outcome, remission, was defined as three consecutive weekly scores of 7 or less on the Hamilton rating scale for depression (17-item). We conducted Cox regression analyses of time to remission and logistic modeling for rates of remission. We tested group differences in Hamilton depression ratings over time via mixed-effects modeling. RESULTS: Remission rates for escitalopram with IPT and with DCM were similar in intention-to-treat (IPT vs. DCM: 58 [95% CI: 46, 71] vs. 45% [33,58]; p = 0.14) and completer analyses (IPT vs. DCM: 58% [95% CI: 44,72] vs. 43% [30,57]; p = 0.20). Rapidity of symptom improvement did not differ in the two treatments. CONCLUSION: No added advantage of IPT over DCM was shown. DCM is a clinically useful strategy to achieve full remission in about 50% of partial responders.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".