Simultaneous Determination of Clobazam and Its Metabolite Desmethylclobazam in Serum by Gas Chromatography with Electron-Capture Detection
Bibliographic record
Abstract
Analysis of clobazam and its metabolites has been performed by gas chromatography and by high-performance liquid chromatography (HPLC). This chapter describes a sensitive and specific gas chromatographic assay with electron-capture detection for the analysis of clobazam and desmethylclobazam using only 100 μL of serum. Using 300 to 500 μL of serum, the same extraction procedure can be used for analysis by HPLC. Pure standard samples of clobazam and desmethylclobazam were provided by Hoechst Company, Canada, and the internal standard, diazepam, by Hoffman-La Roche, Inc., Canada. The liquid-liquid extraction described circumvents usage of toxic chemicals such as benzene and is also applicable to extraction of other benzodiazepines. The extraction solvent mixture, cyclohexane and methylene chloride, is effective only in the proportions used. The same extraction procedure was also used for HPLC analysis, but 400 to 500 ah of sample was required and flunitrazepam was used as the internal standard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".