Skin eruption following the use of two Chinese herbal preparations: a case report.
Bibliographic record
Abstract
OBJECTIVE: To describe a patient who developed a widespread skin eruption following the use of two Chinese herbal medications, Fang Feng Tong Sheng Wan and Bi Yan Pian. CASE SUMMARY: A 34-year-old man developed widespread erythematous papules that coalesced into plaques after five days of therapy with two Chinese herbal preparations. There was no lymphadenopathy or hepatosplenomegaly, and the patient denied any fever, chills or malaise. The skin biopsy was compatible with a drug eruption. When the patient's oral medications, including the herbal medications, were discontinued, the skin eruption resolved over the next few weeks. All of the regular long term medications were restarted without any sequalae. DISCUSSION: This case, once again, emphasizes that, although herbal medications are marketed as natural products, these products can be associated with adverse drug effects. Other adverse effects that have been implicated with the use of Chinese herbal medications include interstitial fibrosis, renal failure, liver toxicity and severe dermatitis. In addition, there are several cases of adulteration of Chinese herbal products with synthetic medications. CONCLUSION: Although rare, Chinese herbal medications can be associated with serious adverse effects. Clinicians should question patients about the use of herbal products whenever an adverse drug effect is suspected.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".