Rapid analysis of metabolites and drugs of abuse from urine samples by desorption electrospray ionization-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
Urine samples obtained from drug abusers were screened for drugs of abuse and their metabolites using DESI-MS and the results obtained were compared to results obtained from GC-MS experiments. The detected analyte classes included amphetamines, opiates, cannabinoids and benzodiazepines. The compounds detected were codeine, morphine, oxymorphone, 11-nor-9-carboxy-Delta(9)-tetrahydrocannabinol, Delta(9)-tetrahydrocannabinol, alprazolam, temazepam, oxazepam, N-desmethyldiazepam (nordiazepam) and hydroxytemazepam. Identities of all the analytes were confirmed by tandem mass spectrometry, matching MS/MS spectra with authentic standard compounds. The concentrations of the analytes in the samples were obtained from semi-quantitative GC-MS studies and were in the range of 270-22,000 ng mL(-1). The analytes could be detected by DESI even after a hundred-fold dilution indicating that the sensitivity of DESI was more than adequate for this study. Selectivity in the DESI-MS measurements for different kinds of analytes could be increased further by optimizing the spray solvent composition: the use of an entirely aqueous solvent enhanced the signal of polar analytes, such as the benzodiazepines, whereas the use of a spray solvent with a high organic content increased the signal of less polar analytes, such as codeine and morphine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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