Interaction with St. John's wort due to exposure with blackcurrant aroma? How not to present a case report of an adverse event
Bibliographic record
Abstract
Background: Adverse events case reports are important for signal generation in pharmacovigilance. They require a thorough collation of the facts, otherwise they may lead to erroneous conclusions which may conceal other treatment-related causes of the observation. Methods: We describe a case report from the literature that arrives at an erroneous conclusion merely from taking insufficient care when collating and interpreting the facts: The authors of the case report confused blackcurrant (Ribes nigrum) with St. John's wort preparation (Hypericum perforatum) and erroneously assumed that the intake of a herbal preparation was responsible for a drop in serum levels of everolimus. Results: The clinical observations in this case report may actually reflect a potentially lethal situation emerging from the prescribed medication everolimus. St. John's wort preparations rich in hyperforin do in fact reproducibly lead to the decrease of blood levels of medications metabolized through cytochrome P450 subtype 3A4. However, a case report requires more care than just ascribing the blame to something seemingly well-known. Conclusion: The readers of this report might have profited more from the description of the risks of treating graft-versus-host disease with everolimus, and the action to be taken in case of potentially severe adverse reactions to everolimus.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".