Segmental Ion Spray LC-MS-MS Analysis of Benzodiazepines in Hair of Psychiatric Patients
Bibliographic record
Abstract
The aim of this study was to develop a liquid chromatography-tandem mass spectrometry (LC-MS-MS) method for the analysis of benzodiazepines in human hair. The method was tested by analyzing hair samples from forensic and clinical psychiatric patients where benzodiazepines had been prescribed during hospitalization and after care. Hair samples were obtained at discharge from the clinic and then after six months. Two-centimeter segments of the hair samples (10-30 mg) were washed once with isopropanol, three times with phosphate buffer, and again with isopropanol, dried, weighed, and digested with proteinase K before solid-phase extraction with BondElut Certify columns. Diazepam, nordiazepam, oxazepam, alprazolam, OH-alprazolam, nitrazepam, 7-aminonitrazepam, flunitrazepam, 7-aminoflunitrazepam, clonazepam, and 7-aminoclonazepam were quantitated in MRM mode using one transition for each analyte and deuterated internal standard. The calibration range was 0.125-5 ng/mg for diazepam, nordiazepam, and oxazepam and 0.025-1.0 ng/mg for the other compounds. In the hair samples analyzed, diazepam, flunitrazepam, nitrazepam, and clonazepam was detected together with their metabolites. Alprazolam was not detected in any sample. Segmental hair analysis revealed differences in drug deposition in hair before and after release from psychiatric treatment. Both increases and decreases of hair drug concentrations were seen after release even though the prescribed dose was the same. This was taken as an indication of noncompliance during the after-care period. We conclude that the extraction and LC-MS-MS procedures were adequate to detect benzodiazepines in hair and that the results indicated that segmental hair analysis might provide retrospective information about medication intake.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".