Comprehensive Drug Screening by Integrated Use of Gas Chromatography/Mass Spectrometry and Remedi HS
Bibliographic record
Abstract
The authors evaluated an integrated approach for the screening of drugs in biosamples consisting of gas chromatography/mass spectrometry analysis of serum or whole blood (SB/GC-MS) and of high-performance liquid chromatographic and ultraviolet (HPLC-UV) analysis of urine with the REMEDi HS Biorad system (U/REM) (Bio-rad; Segrate, MI, Italy). Urine and blood samples from 26 suspected intoxicated patients and from 22 suspected lethal poisoning cases were examined. Eighty-one of the 99 parent drugs/main metabolites detected were identified by SB/GC-MS and 54 with U/REM. Thirty-six drugs/metabolites were identified with both methods, 45 by SB/GC-MS alone, and 18 by U/REM alone. Absence of the mass spectrometry (MS) spectra in the reference library and high polarity of the analytes were the main reasons for failed identification by SB/GC-MS. Unsuccessful identifications with U/REM were basically caused by the absence of the UV spectra in the reference library or by low chromatographic and spectroscopic selectivity as in the case of barbiturates and benzodiazepines (BZD), which represented 11% and 51%, respectively, of the 45 SB/GC-MS unique identifications. Urine samples of 14 BZD-positive cases were also submitted to enzymatic hydrolysis and analyzed with the REMEDi UBz assay, and results were compared with those obtained by SB/GC-MS: 14 of the 22 identified BZD were detected with both methods, three by U/REM only, and five by SB/GC-MS only. In conclusion, the integrated use of SB/GC-MS and U/REM approaches greatly enhances the amount and quality of analytical information obtainable by applying either method alone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".