Diagnosis of Anatoxin-a Poisoning in Dogs from North America
Bibliographic record
Abstract
Anatoxin-a, a toxin produced by several genera of blue-green algae, is considered a potent neurotoxin. Ingestion of water contaminated with the toxin results in acute neurological signs and often death. This report describes fatal cases of anatoxin-a ingestion in 6 dogs, with confirmation of anatoxin-a exposure by liquid chromatography/tandem mass spectrometry (LC-MS/MS/MS). In 1 outbreak, 3 dogs developed seizures and died within an hour after swimming in a river in California, while the other outbreak involved 3 dogs that died within 1 hour after swimming in a pond in Ontario. Anatoxin-a poisoning is rarely reported in dogs as a cause of acute neurological signs and death. However, increased occurrences of blue-green algae blooms in North America make this neurotoxin an important consideration in the diagnosis of sudden death associated with environmental water exposure. This brief communication reports on the isolation and detection of anatoxin-a from environmental water sources and the stomach contents of North American dogs dying of acute neurotoxicosis. This demonstrates the first documented cases of anatoxin-a poisoning in dogs in North America and the importance of LC-MS/MS/MS in identifying neurotoxins responsible for sudden death in cases of suspected blue-green algae toxicosis; especially those cases showing no gross or histological lesions.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".