Bioanalytical Uncertainty Assessment of Ultra‐High‐Performance Liquid Chromatography‐High Resolution Mass Spectrometry Method for Caffeine and Lidocaine in Equine Antidoping: A Dual Perspective on Bottom‐up and Top‐Down Approaches
Bibliographic record
Abstract
The quality of quantitative results in bioanalysis requires not only a validated analytical method but also a rigorous estimation of measurement uncertainty. This study examines the challenges associated with the implementation of two distinct approaches in equine anti-doping control for the assessment of uncertainty associated with an ultra-high-performance liquid chromatography-high resolution mass spectrometry quantitative method for caffeine and lidocaine in horse urine. The bottom-up approach, based on the ISO Guide to the Expression of Uncertainty in Measurement (ISO GUM), was compared to the top-down approach using β-content, γ-confidence tolerance intervals (β,γ-CCTI) via F-test. The key limitation of the ISO GUM method was accurately quantifying the various uncertainty components; it gives standardized uncertainty estimates but requires detailed assumptions and modeling about error sources. The direct application of the GUM method imposes the beforehand correction of the matrix effect to provide reliable results. Parallelly, the chemometric approach β,γ-CCTI offers more flexible and realistic estimations. Four combinations of β and γ were investigated to assess their influence on uncertainty interval width: β = 66.7% and 80%; γ = 90% and 95%; and the method was evaluated under repeatability and intermediate precision conditions through the use of advanced computation that adjusts for matrix effects and proves more straightforward for capturing variability inherent in experimental data. The top-down approach is a reliable alternative for routine use and, particularly, for ensuring compliance with regulatory requirements, with the fact that a known proportion β of future results will be within predefined acceptance limits.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".