Assessment of tandem mass spectrometry and high‐resolution mass spectrometry for the analysis of bupivacaine in plasma
Bibliographic record
Abstract
Triple quadrupole mass spectrometers coupled with high performance liquid chromatography are workhorses in quantitative bioanalyses. They provide substantial benefits including reproducibility, sensitivity and selectivity for trace analysis. Selected reaction monitoring allows targeted assay development but datasets generated contain very limited information. Data mining and analysis of nontargeted high-resolution mass spectrometry profiles of biological samples offer the opportunity to perform more exhaustive assessments, including quantitative and qualitative analysis. The objectives of this study were to test method precision and accuracy, to statistically compare bupivacaine drug concentration in real study samples and to verify if high-resolution and accurate mass data collected in scan mode can actually permit retrospective data analysis, more specifically, extract metabolite related information. The precision and accuracy data presented using both instruments provided equivalent results. Overall, the accuracy ranged from 106.2 to 113.2% and the precision observed was from 1.0 to 3.7%. Statistical comparisons using a linear regression between both methods revealed a coefficient of determination (R(2)) of 0.9996 and a slope of 1.02, demonstrating a very strong correlation between the two methods. Individual sample comparison showed differences from -4.5 to 1.6%, well within the accepted analytical error. Moreover, post-acquisition extracted ion chromatograms at m/z 233.1648 ± 5 ppm (M - 56) and m/z 305.2224 ± 5 ppm (M + 16) revealed the presence of desbutyl-bupivacaine and three distinct hydroxylated bupivacaine metabolites. Post-acquisition analysis allowed us to produce semi-quantitative evaluations of the concentration-time profiles for bupicavaine metabolites.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".