Mad honey intoxication: A case report from eastern Nepal
Bibliographic record
Abstract
Mad honey poisoning, caused by ingestion of honey contaminated with grayanotoxins from specific rhododendron species, poses health risks in regions like Nepal, Turkey, Canada, and Japan. Grayanotoxins bind to sodium channels, leading to prolonged depolarisation and causing bradycardia and hypotension. Historically used in traditional medicine, mad honey’s recent recreational consumption has increased, leading to cases of intoxication from excessive use. We report a case of mad honey poisoning in a 53-year-old woman who consumed approximately 30 mL of honey bought from eastern Nepal, presenting with drowsiness, bradycardia, and hypotension. Despite initial stabilization with atropine, hydrocortisone, and fluid resuscitation, her condition required prolonged monitoring for 72 h. This case highlights the need to consider mad honey poisoning in patients presenting with altered sensorium or unexplained hypotension, even outside endemic regions. Efforts to raise public awareness, regulate honey sales, and improve diagnostic capabilities are essential to prevent and manage future cases. Further studies on toxin variability and rhododendron species in Nepal are also needed. • Rare case of mad honey poisoning reported from eastern Nepal. • Bradycardia and hypotension followed ingestion of 30 mL of contaminated honey. • Managed with atropine, hydrocortisone, and prolonged monitoring for 72 h. • Public health awareness and regulation critical to prevent future intoxications. • Further research needed on toxin variability and rhododendron species in Nepal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".