Valproate augmentation in a subgroup of patients with treatment-resistant unipolar depression
Bibliographic record
Abstract
OBJECTIVES: About 50% of patients with unipolar depression suffer from treatment-resistant depression (TRD). Animal studies have suggested potential antidepressant properties of valproate (VPA) possibly due to its implication in epigenetic programming. METHODS: Fourteen TRD patients (seven males and seven females; age 19-59) received VPA (375-1000 mg/day) in addition to their treatment regimen after previously failing to respond to two or more antidepressant trials and/or different combinations. Clinical response to VPA was investigated prior the treatment (T-0) and after 1 (T-1), 4 (T-4) and 7 (T-7) months of therapy using the Montgomery-Asberg Depression Rating Scale (MADRS) and the Clinical Global Impression (CGI). RESULTS: Compared to T-0, VPA significantly decreased MADRS score at T-1 (P < 0.001), T-4 (P < 0.001) and T-7 (P < 0.001) (partial η(2)=0.86). Importantly, MADRS score at T-7 (13.6 ± 1.6, mean ± SEM) was closer to the reported value of remission (MADRS <10), and none of the patients relapsed during the observational period. Compared to T-0, VPA also decreased CGI-Severity of illness score at T-1 (p = 0.03), T-4 (p < 0.001) and T-7 (p < 0.001) (partial η(2) = 0.74). CONCLUSIONS: Antidepressant augmentation with VPA provided substantial clinical improvement and maintenance over a relatively long-term period in a subgroup of patients with severe TRD. VPA thus deserves further exploration in large double-blind clinical trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".