The role of validated analytical methods in JECFA drug assessments and evaluation for recommending MRLs
Bibliographic record
Abstract
The Joint Food and Agriculture Organization and World Health Organization (FAO/WHO) Expert Committee on Food Additives (JECFA) is one of three Codex committees tasked with applying risk analysis and relying on independent scientific advice provided by expert bodies organized by FAO/WHO when developing standards. While not officially part of the Codex Alimentarius Commission structure, JECFA provides independent scientific advice to the Commission and its specialist committees such as the Codex Committee on Residues of Veterinary Drugs in Foods (CCRVDF) in setting maximum residue limits (MRLs) for veterinary drugs. Codex methods of analysis (Types I, II, III, and IV) are defined in the Codex Procedural Manual as are criteria to be used for selecting methods of analysis. However, if a method is to be used under a single laboratory condition to support regulatory work, it must be validated according to an internationally recognized protocol and the use of the method must be embedded in a quality assurance system in compliance with ISO/IEC 17025:2005. This paper examines the attributes of the methods used to generate residue depletion data for drug registration and/or licensing and for supporting regulatory enforcement initiatives that experts consider to be useful and appropriate in their assessment of methods of analysis. Copyright © 2016 Her Majesty the Queen in Right of Canada. Drug Testing and Analysis © 2016 John Wiley & Sons, Ltd.
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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 | 0.345 | 0.318 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".