Quetiapine augmentation of treatment-resistant depression: a comparison with lithium
Bibliographic record
Abstract
OBJECTIVE: The prevalence of and morbidity associated with treatment-resistant depression has motivated the exploration of treatment alternatives. In this study, quetiapine was compared with lithium in the augmentation of treatment-resistant depression. RESEARCH DESIGN AND METHODS: Open-label, comparative study in 20 patients with major depression who had failed to respond after 4 weeks of treatment with an antidepressant at the maximal recommended dose. Patients were randomised to either lithium or quetiapine in addition to the maximally dosed antidepressant and any other concurrent medications. Lithium was initiated at 600 mg/day; quetiapine was titrated to 400 mg by day 7. RESULTS: Depression, measured by the Hamilton Depression Rating Scale (HAM-D), significantly improved from baseline in both quetiapine (F(1,90) = 25.11, p < 0.0001) and lithium (F(1,90) = 34.54, p < 0.0001). The difference in improvement between the two groups began at day 14 and was seen at all timepoints thereafter (p < 0.05), with the quetiapine group showing greater improvement than the lithium group. In the Montgomery-Asberg Depression Rating Scale (MADRS) analysis, the difference between the quetiapine and lithium group was significant from day 28 onwards (p < 0.05), with subjects improving more in the quetiapine group than the lithium group. The treatment by week interaction showed a significant difference overall between the two groups (p < 0.0001). The severity of psychomotor retardation showed a significant decrease in the Widlocher Psychomotor Retardation Scale scores in the quetiapine (p < 0.0001) and lithium (p < 0.0001) groups. CONCLUSIONS: In this pilot study, quetiapine was an effective augmenting agent in treatment-resistant depression.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".