A General Screening Method for Acidic, Neutral, and Basic Drugs in Whole Blood using the Oasis MCX(R) Column
Bibliographic record
Abstract
Solid-phase extraction (SPE) is becoming a commonly used extraction technique. Most existing SPE methods extract a single drug from a relatively clean biological matrix (e.g., plasma, serum, or urine) using a silica-based column. These methods, however, are generally not satisfactory for forensic applications because the majority of biological samples are not as clean (e.g., whole blood, bile, tissues). Silica-based columns also may have reproducibility and stability problems. Polymer-based columns have been developed to overcome some of these limitations. In this study, sequential extraction of acidic, neutral, and basic drugs from whole blood using a polymer-based column, Oasis MCX, was undertaken. The extraction procedure developed involved a conditioning step using methanol followed by water; a three-step wash sequence using water, 0.1 M hydrochloric acid, then water/methanol (95:5); and two elution steps. One elution step was for acidic and neutral drugs utilizing acetone/chloroform (1:1), and a second used ethyl acetate/ammonium hydroxide (98:2) for basic drugs. Of the drugs tested, 75% were extractable from whole blood and detectable at therapeutic concentrations. Good recoveries and clean extracts were achieved for the basic drugs; however, the extracts were not as clean for acidic drugs. Unfortunately, the Oasis MCX procedure was not suitable for extracting all drugs (e.g., benzodiazepines).
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".