Mechanically tenderized meat
Bibliographic record
Abstract
Background: In 2012, mechanically tenderized meat raised public health concern when an E.Coli 0157:H7 outbreak was linked to the tenderization process. It was discovered that the machinery pushed the E.Coli from the surface of contaminated meat products such as steaks and roasts, into the interior, where it was able to survive the cooking process. Concerns were raised by Lorraine McIntyre and the BCCDC about this issue, and their desire to improve their knowledge base in order to adequately assess the risk. Methods: Data was gathered via a survey conducted electronically and by telephone. Questions were asked to determine the proportion of retail establishments that use their own tenderizing equipment. Questions also asked about other industry practices such as current sanitization and labeling practices. Results: The results of this study were that 24% of surveyed establishments mechanically tenderize their meat products. Of these establishments, 33% have a label that states the meat has been tenderized mechanically and 17% provide cooking instructions on this label. An association was found between mechanically tenderizing meat and establishment type, which suggests that grocery stores are more likely to mechanically tenderize than other establishments, such as restaurants. On the other hand, no association was found between operator experience and their level of knowledge regarding the risks of mechanical tenderization. Conclusions: Overall, this study has demonstrated the likelihood is high that consumers purchase and consume beef that has been mechanically tenderized at the retail level. The results from this study can be used to aid public health officials in quantifying the risk of mechanical tenderization at a retail level and aid in the development and implementation of new legislation such as mandatory labeling of all mechanically tenderized meat.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".