Assessment of veterinary drug residues in food: Considerations when dealing with sub-optimal data
Bibliographic record
Abstract
The use of veterinary drugs in food-producing animals may lead to residues in animal-derived foodstuffs, potentially posing a risk to human safety. While the process of veterinary drug residue risk assessment continues to evolve as new data emerges, a recurring challenge is when sub-optimal or incomplete data are provided with the expectation of supporting a robust risk assessment. The Joint FAO/WHO Expert Committee on Food Additives (JECFA) is comprised of international experts who routinely deal with such data challenges when performing veterinary drug residue evaluations. Recent developments in veterinary drug residue risk assessment are described, including specific consequences of sub-optimal data during the risk assessment process. When feasible, practical solutions to such challenges are also highlighted. Case examples from recent JECFA veterinary drug evaluations are provided to clearly quantify and illustrate the concepts described. The information provided is intended to facilitate the generation of improved quality data, enabling more timely and robust veterinary drug residue risk assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".