St. John's Wort for Major Depressive Disorder: A Systematic Review
Bibliographic record
Abstract
RAND researchers conducted a systematic review that synthesized evidence from randomized controlled trials of St. John's wort (SJW)-used adjunctively or as monotherapy-to provide estimates of its efficacy and safety in treating adults with major depressive disorder. Outcomes of interest included changes in depressive symptomatology, quality of life, and adverse effects. Efficacy meta-analyses used the Hartung-Knapp-Sidik-Jonkman method for random-effects models. Quality of evidence was assessed using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. In total, 35 studies met inclusion criteria. There is moderate evidence, due to unexplained heterogeneity between studies, that depression improvement based on the number of treatment responders and depression scale scores favors SJW over placebo, and results are comparable to antidepressants. The existing evidence is based on studies testing SJW as monotherapy; there is a lack of evidence for SJW given as adjunct therapy to standard antidepressant therapy. We found no systematic difference between SJW extracts, but head-to-head trials are missing; LI 160 (0.3% hypericin, 1-4% hyperforin) was the extract with the greatest number of studies. Only two trials assessed quality of life. SJW adverse events reported in included trials were comparable to placebo, and were fewer compared with antidepressant medication; however, adverse event assessments were limited, and thus we have limited confidence in this conclusion.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".