Early Symptom Improvement as a Predictor of Response to Extended Release Quetiapine in Major Depressive Disorder
Bibliographic record
Abstract
The aim of this post-hoc analysis was to determine whether early symptom improvement with extended release quetiapine (quetiapine XR) may predict treatment outcome in patients with major depressive disorder. Data were from 6, double-blind, placebo-controlled studies of quetiapine XR (2 fixed-dose and 2 flexible-dose monotherapy and 2 adjunct studies) in adult patients with major depressive disorder. Montgomery-Åsberg Depression Rating Scale (MADRS) and Clinical Global Impression-Severity Score (CGI-S) were assessed at baseline, weeks 2, 4, and 6. Hamilton Rating Scale for Depression (HAM-D) was assessed at baseline and week 6. The MADRS improvement at week 2 (15%, 20%, 25%, 30%) was used to predict response and remission, based on MADRS (50% improvement; total score ≤ 12) or HAM-D (50% improvement; total score ≤ 7). The CGI-S improvement (1 point) at week 2 was used to predict final outcome (CGI-S score ≤ 2). The predictive value for early improvement with quetiapine XR was found to be "very strong" (Yule's Q coefficient, a combined measure of sensitivity and specificity) using 30% MADRS improvement as the threshold. This was relatively comparable for response and remission and for fixed-dose, flexible-dose, and adjunct studies. This was also observed for placebo. Exceptions were: adjunct studies (where predictivity was lower for ongoing antidepressant/placebo), and for remission (predictivity for remission appeared lower than for response with placebo). In conclusion, outcome at week 6 with quetiapine XR for a major depressive episode could be predicted by 30% improvement after 2 weeks, a finding that could give doctors confidence to continue treatment and may facilitate adherence in patients.
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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 | 0.002 | 0.004 |
| 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".