Modafinil in Forensic and Clinical Toxicology—Case Reports, Analytics and Literature
Bibliographic record
Abstract
Modafinil is used because of its wakefulness-promoting properties for treatment of diseases associated with extreme sleepiness (i.e., narcolepsy). Additionally, it is misused as a "cognitive enhancer" to increase alertness and to improve concentration. We present modafinil concentrations in serum samples in five cases of our routine work measured by high-performance liquid chromatography coupled with a photo diode array detector after solid-phase extraction. One sample was analyzed for clinical toxicology purposes. The other four were investigated for the police: three cases of driving under the influence of drugs and one case of bodily harm. Sample preparation consisted of solid-phase extraction using Bond Elut® C18 columns. Papaverine was used as internal standard. Chromatographic separation was carried out using a Polaris C18-A column in an isocratic run. Wavelengths used for UV-detection were 220 nm for modafinil and 239 nm for the internal standard, respectively. The method was validated with a reduced validation design for rare analytes. A six-point-calibration from 0.5 to 5.0 mg/L, covering the therapeutic range (0.9-3.3 mg/L), was used for quantification. Concentrations in serum were in the range of 1.3 to ~34 mg/L (median: 3.6 mg/L; mean: 9.0 mg/L). To our knowledge, there are only few publications concerning the serum concentrations of modafinil in cases of (suspected) misuse, forensic cases or intoxications. In our discussion, the serum concentrations we determined are compared with the levels described in the literature so far.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".