St. John's Wort St. John's Wort (Hypericum perforatum) as an Alternative Treatment for Depression
Bibliographic record
Abstract
Depression is one of the most common mental disorders worldwide that has a significant impact on the quality of life of individuals and society. Depression has recently become one of the mental health illnesses that many people in Indonesia experience, but Indonesian people do not yet have concerns about the treatment of depression, especially mild depression. Conventional depression treatments that already exist, such as selective serotonin reuptake inhibitor (SSRI) antidepressants, have been proven effective, but have side effects so that people are afraid to undergo treatment. St. John's Wort (Hypericum perforatum) is one of the alternative treatments for depression that is popular throughout the world, but in Indonesia there is no herbal medicine industry company that has developed this plant as a treatment for mild depression with herbs. This review aims to review the effectiveness and safety of St. John's Wort as a treatment for depression, as well as discuss its potential benefits and limitations. Based on a literature review, St. John's Wort has been shown to be effective in reducing symptoms of depression in patients with mild to moderate depression. However, of course, it still needs to be used with caution because of the potential for side effects and interactions with other drugs. Thus, St. John's Wort can be an alternative treatment for depression with new herbs in Indonesia that is effective, but it needs to be used under strict supervision.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".