Evaluation of a commercial LC-MS/MS assay for the quantification of ethyl glucuronide in urine for alcohol intake monitoring
Bibliographic record
Abstract
Abstract Background: A commercially available in vitro diagnostics (IVD)-approved mass spectrometric assay for the quantification of ethyl glucuronide (EtG) and ethyl sulfate (EtS) in urine (RECIPE Chemicals+Instruments GmbH, Munich, Germany) was verified for monitoring of recent alcohol intake after transplantation. Methods: For sample preparation, 50 μL of urine sample was mixed with an isotope-labeled internal standard solution. After centrifugation, 5 μL of the supernatant was analyzed by LC-MS/MS in a total run time of 3 min. An API 6500 tandem mass spectrometer (AB SCIEX, Toronto, Canada) combined with a Shimadzu UFLC system (Duisburg, Germany) was applied. Results: The limits of quantification for the commercial assay were 0.07 mg/L for EtG and 0.03 mg/L for EtS in urine. The coefficient of variation for both analytes was lower than 7% (within-day) and 15% (between-days). Accuracy ranged between 101 and 144% for samples from an external quality assurance program. The comparison of the commercial test kit and an established LC-MS/MS method showed a very good agreement for EtG (r=0.96) and EtS (r=0.97) over a broad urine concentration range. Conclusions: The commercial IVD-certified LC-MS/MS assay is suitable for the analysis of EtG and EtS in human urine[0] to assess recent alcohol intake in transplant monitoring.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".