Predictors of functional response and remission with desvenlafaxine 50 mg and 100 mg: a pooled analysis of randomized, placebo-controlled studies in patients with major depressive disorder
Bibliographic record
Abstract
OBJECTIVE: The value of early functional improvement at week 2 for predicting subsequent functional outcomes at week 8 was assessed in a pooled analysis of patients with major depressive disorder (MDD) treated with desvenlafaxine (50 or 100 mg/d) or placebo. METHODS: Data were pooled from eight double-blind, placebo-controlled studies of desvenlafaxine 50 mg/d or 100 mg/d for the treatment of MDD. Optimal week-2 improvement thresholds in Sheehan Disability Scale (SDS) score, which best predicted week-8 treatment success, were determined using receiver operating characteristic (ROC) analysis. Four definitions of treatment success were established: (1) functional response, (2) functional/depression response, (3) functional remission, and (4) functional/depression remission. Odds ratios (ORs) of early improvement for prediction (based on thresholds determined in the ROC analysis) of week-8 treatment success were computed using logistic regression models. RESULTS: Functional early improvement thresholds of 17%-32% were predictive of week-8 treatment success across treatment groups and definitions of treatment success. Optimal thresholds were higher for more stringent definitions. Negative predictive value exceeded positive predictive value, indicating that failure to achieve early functional improvement was more informative about later treatment success than was the achievement of early functional improvement. Early change in SDS was a highly significant predictor of functional response/remission (ORs, 4.981-8.737; all p < 0.0001); the interaction between treatment and early functional improvement was not significant. CONCLUSION: Early improvement in SDS total score was predictive of functional outcomes for patients treated with desvenlafaxine 50 mg, desvenlafaxine 100 mg, or placebo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".