Exploration of the known foodborne enteric health risks associated with plant-based and/or simulated meat, poultry, and seafood products: A scoping review protocol
Bibliographic record
Abstract
Background \nFoodborne enteric pathogens significantly contribute to the number of illnesses, hospitalizations, and deaths in Canada, affecting approximately 4 million Canadians annually. The FoodNet Canada program employs active surveillance of human, water, farm, and retail (meat and produce) sources to identify the burden of enteric disease on Canadian communities. Despite the increasing popularity of plant-based and simulated meat, poultry and seafood globally, little is known about the risk of enteric illness associated with these products. Hence, this scoping review aims to explore the available literature and determine the following: (i) the current understanding of global foodborne health risks associated with plant-based and simulated meat, poultry, and seafood products; (ii) the enteric pathogens of concern associated with these products; and (iii) the utility of adding these products to the retail surveillance component of FoodNet Canada. \nMethods/Design \nScreening will be conducted in two phases based on the determined eligibility criteria. Both primary research and grey literature, in English or French, detailing either a study, an outbreak, a recall, or a contamination event due to an enteric pathogen for plant-based/simulated meats, poultry, or seafood products will be considered for inclusion. There are no inclusion criteria based on the type of article included. Searches will be conducted in PubMed ®, Scopus ®, Embase ®, and Web of Science TM. Grey literature sources such as Google Scholar and the websites of relevant international public health and food safety organizations will also be searched. \nCharting Methods \nData charting will include the following: study characteristics; identification of the enteric pathogens; classification of the affected product as either plant-based or simulated meat/poultry/seafood; cause of product contamination; and other applicable characteristics associated with the outbreak, recall, or risk of the product. Findings will be summarized through narrative synthesis and presented through a series of tables and figures.
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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 | 0.060 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.008 |
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".