Fatal <i>Amanita muscaria</i> poisoning in a dog confirmed by PCR identification of mushrooms
Bibliographic record
Abstract
Diagnosing mushroom poisoning in dogs can be difficult and often includes identification of suspect mushrooms. Visual identification may be hindered by mastication, oral medications, or poor quality of environmental mushroom samples. Other analytical techniques may thus be necessary to aid in mushroom identification. A 5-y-old neutered male Labrador Retriever dog developed acute onset of vomiting, diarrhea, tremors, seizures, and somnolence. The dog was treated at a veterinary clinic and was briefly stabilized, but died during transport to an emergency clinic. On postmortem examination at the University of Kentucky Veterinary Diagnostic Laboratory, the dog's stomach was full of mushrooms covered with activated charcoal. Mushrooms were damaged, fragmented, and discolored, precluding accurate visual identification. Mushroom pieces were sent to the Department of Plant Pathology at the University of California-Davis for PCR identification; the neurotoxic mushroom Amanita muscaria was identified. A qualitative liquid chromatography-mass spectrometry (LC-MS) method was developed to detect ibotenic acid and muscimol, the toxic compounds present in A. muscaria. Mushrooms, stomach contents, and urine were analyzed by LC-MS; ibotenic acid and muscimol were detected in all samples. Because identification of ingested mushrooms is sometimes necessary to confirm mushroom poisoning, PCR can identify ingested mushrooms when visual identification is unreliable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".