BAGI-assessed green GC-MS method for rapid analysis of paracetamol/metoclopramide in pharmaceuticals and plasma
Why this work is in the frame
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Bibliographic record
Abstract
The growing demand for eco-friendly and cost-effective analytical methods has driven the development of a fast, green, and sensitive GC-MS assay for the simultaneous quantification of paracetamol (PAR) and metoclopramide (MET) in pharmaceutical formulations and human plasma. Separation was achieved in 5 min using a high-polarity 5% Phenyl Methyl Silox column, with detection at *m/z* 109 (PAR) and 86 (MET). The method was fully validated per ICH guidelines, showing excellent linearity (PAR: 0.2-80 µg/mL, r² = 0.9999; MET: 0.3-90 µg/mL, r² = 0.9988) and precision (tablet recovery: 102.87 ± 3.605% PAR, 101.98 ± 3.392% MET; plasma recovery: 92.79 ± 1.521% PAR, 91.99 ± 2.153% MET). Greenness assessment via three metrics, including the BAGI tool (score: 82.5), confirmed its environmental superiority over conventional methods. With high sensitivity, accuracy, and a 5-minute runtime, this approach is ideal for routine quality control and pharmacokinetic studies.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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