Two cases of poisoning by raw taro leaf and how a poison control centre, food safety inspectors, and a specialty supermarket chain found a solution
Bibliographic record
Abstract
Although the taro plant, Colocasia esculenta, is commonly consumed throughout Asia, Africa, the Pacific Islands, and the Caribbean, its consumption is less common in North America. Exposure to raw or improperly prepared taro is associated with oropharyngeal irritation and swelling and, rarely, airway obstruction. Although cases of toxicity in countries where taro is a staple have been reported, cases in North America have not been described. Here, two cases of oral irritation and swelling in BC residents who ate raw taro leaf, were reported to the British Columbia Drug and Poison Information Centre (BC DPIC) and triggered an investigation involving a regional health authority and the Canadian Food Inspection Agency. Investigators found that the vendor, a chain of ethnic supermarkets, offered no point of sale preparation instructions. The vendor responded initially by posting instructional signage and later by voluntary product withdrawal. Analysis of BC DPIC records between 1 November 2011 and 20 December 2013 identified 11 cases of symptomatic taro exposure, five to the leaf and six to the corm. The two index cases and subsequent investigation illustrate how new foods or foods in unfamiliar contexts may present as calls to a poison control centre and that prevention requires collaboration among public and corporate stakeholders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".