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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".