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Record W4408856094 · doi:10.1016/j.hmedic.2025.100189

Mad honey intoxication: A case report from eastern Nepal

2025· article· en· W4408856094 on OpenAlexaboutno aff
Akash Roy, Sumit Kumar Singh, Suman Rijal, Anjan Nepali

Bibliographic record

VenueMedical Reports · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyTraditional medicineMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.010
GPT teacher head0.243
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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