Veterinary drug residues in animal-derived foods: occurrence, veterinary legislation and perceived risk factors in Cameroon
Bibliographic record
Abstract
The presence of veterinary drug residues in animal-derived foods (ADF) remains a public health concern in low-income countries such as Cameroon. This paper provides an overview of the current status of antimicrobial (AM) residues in ADF, veterinary legislation on the use of AM and perception of risk factors with emphasis on the need for sustainable management in Cameroon from a one health perspective. Results show that a wide range of antimicrobials is used in the country with little or no attention to good veterinary practices. Residues of commonly used AM agents including those banned for use in food animal production in high-income countries were reported. The current legislation on the use of veterinary drugs is weak and does not make provision for key concepts such as Maximum Residue Limit. Veterinarians argue that the lack of disease diagnostic facilities and excessive use of AM has led to the presence of residues in ADFs. The government and relevant agencies need to enforce regulations for the use of veterinary drugs. Further, awareness creation through educational campaigns for users and consumers as well as the implementation of measures to restrict prescription and dispensation of AM agents to recognised veterinarians are necessary. More studies on AM residues in ADFs are needed to support veterinary drug surveillance policies. This paper strongly suggests collaboration between food safety experts, animal and human health professionals as well as policymakers to help implement good surveillance of antimicrobial use and to safeguard potent AM suitable for disease control for forthcoming generations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".