Effects of once-daily adjunct quetiapine XR on sleep disturbance in patients with mdd: a pooled analysis from two acute studies
Bibliographic record
Abstract
Introduction Disrupted sleep is common in depression. Objectives Investigate effects of adjunct extended release quetiapine fumarate (QTP-XR) on sleep disturbance and quality in patients with MDD and inadequate response to antidepressant (AD) therapy. Methods Data were pooled from two (D1448C00006/D1448C00007) 6-week, double-blind, randomised, placebo (placebo+AD)-controlled studies of adjunct QTP-XR (15 0 mg/day and 300 mg/day). Primary endpoint: MADRS total score change versus placebo+AD. Secondary endpoints (post hoc): change from randomisation in MADRS item 4 (reduced sleep), HAM-D items 4, 5 and 6 (early-, middle- and late-insomnia), sleep disturbance factor (HAM-D items 4+5+6) and sleep quality (PSQI global score). MADRS total score change in patients with baseline HAM-D sleep disturbance factor score > = 4 or < 4 (high and low sleep disturbance, respectively) was evaluated. Results 919 patients received adjunct QTP-XR: 150 mg/day (n = 309), 300 mg/day (n = 307), placebo+AD (n = 303). At Week 6, adjunct QTP-XR (both doses) reduced MADRS item 4, HAM-D sleep disturbance factor, HAM-D items 4, 5 and 6 and PSQI global scores from baseline versus placebo+AD (p < 0.001). In patients with baseline HAM-D>=4 (n = 226, 215 and 210, respectively) adjunct QTP-XR (both doses) improved (p< 0.01) MADRS total score versus placebo+AD from Week 1 onward. In patients with baseline HAM-D< 4 (n = 83, 92, 93, respectively) adjunct QTP-XR (both doses) improved (not statistically significantly) MADRS total score versus placebo+AD at Week 6. Conclusions Adjunct QTP-XR significantly restored sleep and improved sleep quality in patients with MDD and inadequate response to AD. Significant improvement in depressive symptoms was demonstrated with adjunct QTP-XR in patients with MDD and high baseline sleep disturbance. AstraZeneca funded.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".