Bibliographic record
Abstract
Despite all the uncertainty surrounding the nature and efficacy of St. John's Wort, its use has skyrocketed in the past decade. This has created great concern among the scientific and healthcare communities, particularly in light of recent research on the numerous potential drug interactions of this herbal supplement. The F.D.A. issued a warning in February 2001 about the possibility of SJW decreasing the effectiveness of numerous prescription drugs, and recent reports show that the concomitant use of SJW lowers the plasma concentrations of some drugs. Decreased serum concentrations of cyclosporin, warfarin, indinavir, Digoxin, oral contraceptives, migraine medications, theophylline, and other HIV-1 protease inhibitors have all been reported. There are two mechanisms of action of SJW that are thought to be responsible for the increased metabolism - and the commensurate decrease in effectiveness - of these drugs: SJW is believed to enhance the activity of Cytochrome P450 enzymes, as well as the activity of the drug efflux transporter P-glycoprotein. Such findings are compelling the F.D.A. to act quickly in requiring herbal manufacturers to begin labeling bottles of SJW with a clear warning about these possible drug interactions. However, much more needs to be done in educating healthcare professionals to actively seek awareness of their patients' use of SJW through routine inquiries about their use of all herbal remedies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.202 | 0.084 |
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".