Magnocaine: Physical Compatibility and Chemical Stability of Magnesium Sulphate and Lidocaine Hydrochloride in Prefilled Syringes
Bibliographic record
Abstract
OBJECTIVE: To evaluate the physical compatibility and chemical stability of mixtures of magnesium sulphate and lidocaine in order to determine the feasibility of manufacturing a prefilled syringe combining these two drugs for use as an intramuscular (IM) loading dose for eclampsia prevention and/or treatment. This ready-to-use mixture will provide a more tolerable and accessible route of administration appropriate for widespread use. METHODS: reconstituted from salt (61%), with lidocaine hydrochloride (2%) were evaluated every 14 days over six months. The concentration of lidocaine was determined by a stability indicating high performance liquid chromatographic method, while the concentration of magnesium was determined by an automated chemistry analyzer. RESULTS: No changes in pH, color or precipitates were observed for up to 6 months. The 95% confidence interval of the slope of the curve relating concentration to time, determined by linear regression, indicated that only the admixtures of commercially-available magnesium sulfate and lidocaine as well as the 61% magnesium sulfate solution (reconstituted from salt) maintained at least 90% of the initial concentration of both drugs at 25°C and 40°C at 6 months. CONCLUSIONS: Commercially available MgSO4 and lidocaine hydrochloride, when combined, are stable in a pre-filled syringe for at least six months in high heat and humidity conditions. This finding represents the first step in improving the administration of magnesium sulphate in the treatment and prevention of eclampsia in under-resourced settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".