Ultrafast Screening of Synthetic Cannabinoids and Synthetic Cathinones in Urine by RapidFire-Tandem Mass Spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Screening for emerging drugs of abuse, specifically synthetic cathinones and synthetic cannabinoids, is difficult for high-throughput laboratories as immunoassay kits are often unavailable. Consequently, most laboratories employ liquid chromatography-tandem mass spectrometry (LC-MS-MS) screening, which can be complex and time consuming as these techniques may require involved sample preparation and lengthy analysis times. The increasing demand for novel psychoactive substance testing necessitates alternative screening methods that are sensitive, fast and versatile. The RapidFire tandem mass spectrometry system (RF-MS-MS) provides a rapid and highly specific screen for these emerging drugs of abuse with minimal sample preparation and an instrumental analysis time of <14 s per sample. Presented here are two RF-MS-MS screening methods used to analyze 28 emerging drugs of abuse, 14 synthetic cannabinoids and 14 synthetic cathinones, in urine with run times of 9 and 12.6 s, respectively. Sample preparation and hydrolysis were performed in a 96-well plate with one multiple reaction monitoring transition used for the identification of each compound. Eighteen thousand urine specimens were screened by liquid-liquid extraction followed by LC-MS-MS analysis, and the results were compared with those obtained using the RF-MS-MS screening method. The analytical data illustrate the advantages of the RF-MS-MS methods.
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it