Predictors of functional response and remission with desvenlafaxine 50 mg/d in patients with major depressive disorder
Bibliographic record
Abstract
BACKGROUND: The predictive value of early functional improvement for treatment success at week 8 was assessed in a pooled analysis in patients with major depressive disorder (MDD). METHODS: Data were pooled from 7 double-blind studies in adult patients with MDD randomly assigned to desvenlafaxine 50 mg/d or placebo. Four levels of treatment success were determined at week 8 for patients with baseline Sheehan Disability Scale (SDS) score > 12 (N = 2156): functional response (SDS ≤12 and ≥50% improvement in SDS), functional/depression response (SDS ≤12 and ≥50% improvement in both SDS and 17-item Hamilton Rating Scale for Depression [HAM-D17] score), functional remission (SDS < 7), and functional/depression remission (SDS < 7 and HAM-D17 ≤7). Week 2 improvement in SDS was evaluated as a predictor of later functional response/remission using receiver operating characteristic analysis. Odds ratios (ORs) of the predictability of improvement thresholds were computed from a logistic regression model. RESULTS: The proportion of patients achieving each level of treatment success was significantly greater for patients treated with desvenlafaxine (40%, 32%, 23%, 15%, respectively) vs placebo (31%, 22%, 17%, 10%; all P ≤ 0.002). Early change in SDS was a highly significant predictor of functional response/remission (ORs, 0.958-0.970; all P < 0.0001). Discussion Patients' early functional response to desvenlafaxine 50 mg/d is predictive of treatment success.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".