A comparison of patterns of spontaneous adverse drug reaction reporting with St. John's Wort and fluoxetine during the period 2000–2013
Bibliographic record
Abstract
Herbal medicines are perceived to be safe by the general public and medical practitioners, despite abundant evidence from clinical trials and case reports that show herbal preparations can have significant adverse effects. The overall impact of adverse events to herbal medicines in Australia is currently unknown. Post marketing surveillance of medications through spontaneous adverse drug reaction (ADR) reports to the Therapeutic Goods Administration (TGA) is one way to estimate this risk. The patterns of spontaneously reported ADRs provide insight to herbal dangers, especially when compared with patterns of a mechanistically similar conventional drug. The study compared the pattern of spontaneously reported ADRs to St. John's Wort (Hypericum perforatum), a common herbal treatment for depression which contains selective serotonin reuptake inhibitors (SSRI), to fluoxetine, a commonly prescribed synthetic SSRI antidepressant. Spontaneous ADR reports sent to the TGA between 2000-2013 for St. John's Wort (n = 84) and fluoxetine (n = 447) were obtained and analysed. The demographic information, types of interaction, severity of the ADR, and the body systems affected (using the Anatomical Therapeutic Chemical classification system) were recorded for individual ADR cases. The majority of spontaneously reported ADRs for St. John's Wort and fluoxetine were concerning females aged 26-50 years (28.6%, 22.8%). The organ systems affected by ADRs to St John's Wort and fluoxetine have a similar profile, with the majority of cases affecting the central nervous system (45.2%, 61.7%). This result demonstrates that herbal preparations can result in ADRs similar to those of prescription medications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".