Characterization, optimization, and selection of identification criteria for LC–QTOF–MS
Bibliographic record
Abstract
The establishment of stringent identification criteria is essential for accurate reporting of toxicological drug testing, particularly in forensic settings involving medico-legal cases. Liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) is widely employed for its broad analyte coverage and high mass accuracy, yet limited published and validated identification criteria pose significant challenges for its use beyond presumptive screening in low case volume settings. This study characterized, optimized, and selected LC-QTOF-MS identification criteria, assessing the influence of concentration, matrix and drug class on their performance. In addition to standard identification parameters, an effective combined weight score (CWS) threshold that emphasized library score and mass error was established. Higher analyte concentrations improved spectral reproducibility, while urine matrices introduced variability in isotope ratios and library scores. Authentic casework demonstrated 99.9% efficiency, 98.9% sensitivity, and 100% specificity, indicating a highly reliable method that achieves excellent accuracy, minimizes false positives as required for confirmatory techniques, and maintains sufficient sensitivity for effective screening of casework, thereby supporting robust and defensible forensic toxicology workflows. These findings also highlight the importance of refining LC-QTOF-MS specific identification criteria to enhance consistency and reliability in forensic toxicology reporting and allows for reproducibility across other instrumentation, workflows, and fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".