Valproate augmentation in a subgroup of patients with treatment-resistant unipolar depression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 it