Simultaneous determination of ethamsylate, tramadol and lidocaine in human urine by capillary electrophoresis with electrochemiluminescence detection
Bibliographic record
Abstract
Ethamsylate, tramadol and lidocaine, partly excreted by the kidney, are generally used as hemostatic, analgesic and local anesthetic in surgery. We developed a simple and sensitive method for their simultaneous monitoring in human urine based on CE coupled with electrochemiluminescence detection by end-column mode. Under optimized conditions the proposed method yielded linear ranges from 5.0 x 10(-8) to 5.0 x 10(-5), 1.0 x 10(-7) to 1.0 x 10(-4) and 1.0 x 10(-7) to 1.0 x 10(-4) M with LODs of 8.0 x 10(-9) M (36 amol), 1.6 x 10(-8) M (72 amol) and 1.0 x 10(-8) M (45 amol) (S/N = 3) for ethamsylate, tramadol and lidocaine, respectively. The RSD for their simultaneous detection at 1.0 x 10(-6) M was 2.1, 2.8 and 3.2% (n = 7), respectively. For practical application an extraction step with ethyl acetate at pH 11 was performed to eliminate the influence of the sample ionic strength. The recoveries of ethamsylate, tramadol and lidocaine at different levels in human urine were between 87 and 95%. This method was used for simultaneous detection of ethamsylate, tramadol and lidocaine in clinic urine samples from two medicated patients. It was valuable in clinical and biochemical laboratories for monitoring these drugs for various purposes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".