Simultaneous Quantitation of Opioids in Blood by GC-EI-MS Analysis Following Deproteination, Detautomerization of Keto Analytes, Solid-Phase Extraction, and Trimethylsilyl Derivatization
Bibliographic record
Abstract
Seven opioid analytes including codeine, morphine, 6-acetylmorphine, hydrocodone, hydromorphone, oxycodone, and oxymorphone were detected in postmortem blood (n > 1000). Two milliliters of specimen was deproteinated with approximately 2.5 mL of methanol and derivatized with hydroxylamine before solid-phase extraction and derivatization with BSTFA + 1% TMCS. Extracts were assayed by gas chromatography-electron impact-mass spectrometry utilizing selected ion mode. One-microliter aliquots were injected onto an HP-1MS capillary column (30 m x 0.25-mm i.d., 0.25 microm) with a helium linear velocity of 62 cm/s. Temperature programming began at 160 degrees C (hold 0 min), then increased at rates of 35 degrees C/min to 195 degrees C, 5 degrees C/min to 240 degrees C, and 30 degrees C/min to 300 degrees C (hold 2 min) resulting in a total run time of 14-min. Quantitative determinations were based on the ratios of the analyte peak areas to the corresponding deuterated analogues. Calibration curves were linear for the following concentrations: 10-500 ng/mL (6-AM), 100-2000 ng/mL (oxycodone), and 50-1000 ng/mL (all other opioids). LOQs ranged from 5 ng/mL (6-AM) to 20 ng/mL (oxycodone). Between-run precision yielded CVs ranging from 2.79% to 5.34% (n = 12). These data suggest that methanolic deproteination and dual derivatization improve separation and simultaneous quantitation of seven opioid analytes in difficult matrices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".