Bibliographic record
Abstract
Depression and anxiety are among the top 10 health problems for which complementary and alternative therapies (CATs) are most frequently used, and medicinal herbs are among the most popular of these treatments. St. John's wort (Hypericum perforatum) is a perennial herb that has become a widely used depression therapy. Extracts of hypericum have shown affinity for receptors within multiple neurochemical systems. The primary active substance responsible for the antidepressant effect is not well defined, but most work has concentrated specifically on the hypericin and hyperforin components. Although hypericum has demonstrated significant antidepressant and antianxiety effects in multiple studies, there are several recent studies that do not support the previous evidence. In all reported studies, hypericum extracts have been well tolerated. In addition, new psychiatric uses for hypericum in obsessive-compulsive disorder, generalized anxiety disorder, menopausal symptoms, and alcohol dependence have been reported. Because patients are choosing to pursue CAT as a first-line therapy, psychiatrists will need to have a better understanding of phytomedicines used for treating depression and anxiety, and thus be better prepared to serve as effective allies of their patients.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".