Predictors of functional improvement in employed adults with major depressive disorder treated with desvenlafaxine
Bibliographic record
Abstract
We carried out a secondary analysis of a double-blind, placebo-controlled trial of desvenlafaxine for major depressive disorder (MDD) to explore the associations between depressive symptoms and subtypes, and functional outcomes, including work functioning. Employed outpatients with MDD were assigned randomly in a 2 : 1 ratio to receive desvenlafaxine 50 mg/day or placebo for 12 weeks. Analyses were carried out post-hoc with the intent-to-treat (ITT) sample (N=427) and a prospectively defined modified ITT sample (N=310), composed of patients with baseline 17-item Hamilton Rating Scale for Depression score of at least 20. Functional outcomes at week 12 included items and factors from the Montgomery-Åsberg Depression Rating Scale, Sheehan Disability Scale, and the Work Productivity and Activity Impairment questionnaire. In the modified ITT sample, but not in the ITT sample, desvenlafaxine-treated patients showed significantly greater improvement in several functional outcomes in the responder, nonanxious, and normal-energy patient subgroups. Improvement in the 17-item Hamilton Rating Scale for Depression total score at week 2 predicted change at week 12 in several functional outcomes. Functional improvement at 12 weeks was greater in subgroups of patients and was also significantly predicted by early improvement in depressive symptoms in employed patients with MDD treated with desvenlafaxine.
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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 | 0.002 |
| 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 | 0.000 | 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".