Simultaneous Quantitation of Opioids in Blood by GC-EI-MS Analysis Following Deproteination, Detautomerization of Keto Analytes, Solid-Phase Extraction, and Trimethylsilyl Derivatization
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
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it