Categorical improvement in functional impairment in depressed patients treated with desvenlafaxine
Bibliographic record
Abstract
OBJECTIVE: This post-hoc pooled analysis evaluated categorical change in functional impairment in patients with major depressive disorder (MDD) treated with desvenlafaxine versus placebo and examined whether early improvement in functioning predicted functional outcomes at study endpoint. METHODS: Data were pooled from eight randomized, double-blind, placebo-controlled studies of desvenlafaxine for the treatment of MDD, including adults who were randomly assigned to receive desvenlafaxine 50 or 100 mg/d or placebo (N=3,384). Shift tables were generated for categorical changes in functional impairment from baseline based on Sheehan Disability Scale (SDS) subscale scores. The categories were none/mild (0-3), moderate (4-6), and marked/extreme (7-10). Treatment comparisons for prespecified shifts of interest and predictive value of week 2 or 4 improvement in SDS subscale scores for functional outcome at week 8 were assessed using logistic regression. RESULTS: Greater proportions of patients receiving desvenlafaxine 50 and 100 mg achieved improvement from baseline to week 8 for each prespecified shift endpoint versus placebo (all p ≤ 0.02). Early improvement in SDS subscale scores was a statistically significant predictor of functional outcome at week 8, both overall and for each treatment group (all p<0.0001). CONCLUSIONS: Treatment with desvenlafaxine 50 or 100 mg/d led to significantly greater categorical improvement in functional impairment versus placebo, and improvement in SDS subscale scores significantly predicted functional outcome. Monitoring patient progress early in the course of antidepressant treatment using a functional assessment such as the SDS may help clinicians determine whether or not treatment adjustments are needed.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".