A validated screening and confirmation method for 946 drugs and metabolites using LC–QTOF-MS with SWATH acquisition
Bibliographic record
Abstract
A streamlined liquid chromatography quadrupole time-of-flight mass spectrometry method utilizing protein precipitation and filtration extraction was developed to achieve rapid and reliable screening and confirmation for blood and urine matrices. This method targets 946 drugs and metabolites across 35 drug classes via sequential window acquisition of all theoretical mass spectra with variable customized windows to enhance spectral clarity, and was validated per established guidelines to ensure high accuracy and reproducibility. Combined with complementary in-house methods, this approach meets and exceeds the testing requirements outlined in ANSI/ASB standards and recommendations for postmortem, drug-facilitated crime, and Tier I and II driving under the influence of drug analyses. The method demonstrated efficient and sensitive performance, achieving limits of detection as low as 0.1 ng/mL. It accurately identified expected detections across 67 proficiency test samples and 224 authentic case samples, with high accuracy and reliability in the detection of both traditional drugs and novel psychoactive substances. The method employs an in-house built library and incorporates in-batch standards analyzed alongside case samples to ensure contemporaneous identification criteria, making it suitable for confirmation and reporting purposes. By expanding the analytical capabilities to include a vast range of analytes, this method improves the likelihood of identifying substances that may otherwise go undetected and reduces the need for multiple separate tests, thereby enhancing the overall effectiveness of toxicological investigations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".