Risk assessment supporting the establishment of a maximum residue limit for ractopamine in beef liver, applicable in the Arab Republic of Egypt
Bibliographic record
Abstract
In 2012, the Codex Alimentarius Commission adopted maximum residue limits (MRLs) for ractopamine in pig and cattle tissues. Egypt, a country that records a high consumption of beef liver, conducted a health risk assessment to estimate the risks associated with the adoption of Codex MRLs and the possible adoption of alternative values that may offer higher protection. Ractopamine was characterized based on previous assessments performed by international regulatory agencies, and an acceptable daily intake was set at 1 µg/kg bw for both chronic and acute ractopamine exposure. Beef liver consumption data for the Egyptian population were collected through a field survey (529 households, 1929 individuals). The standard body weight of 60 kg was used, as well as 70 kg, as a potentially more representative weight for the Egyptian population. Simulations showed that when the MRL for ractopamine in beef liver is set to 40 µg/kg (Codex MRL) or 20 µg/kg, the health-based guidance value of 1 µg/kg bw was not exceeded, as a result of chronic or acute exposure. An MRL of 20 µg/kg of ractopamine in beef liver was shown to provide optimum protection of Egyptian consumers, considering other potential sources of ractopamine intake and abnormally high consumption patterns, and was therefore recommended for adoption in Egypt. This study presents the inputs, model, and results of the probabilistic risk assessment that supported such recommendation. PRACTICAL APPLICATION: Residues of veterinary drugs, such as ractopamine, accumulate in animal tissues and may pose a risk to consumers. Establishing maximum residue limits (MRLs) will help importers by giving them the necessary visibility for commercial trade. It will also benefit Egyptian consumers, large consumers of beef liver, who will be better protected with a lower MRL than the internationally recommended one.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".