Magnesium as an Effective Adjunct Therapy for Drug Resistant Seizures
Bibliographic record
Abstract
OBJECTIVE: To explore the use of magnesium (Mg), an endogenous ion and enzymatic co-factor used in a variety of medical applications, for the treatment of epileptic seizures resistant to traditional medical therapy. BACKGROUND: For almost a century, Mg has been used as prophylaxis and treatment of seizures associated with eclampsia. Mg is a CNS depressant, with numerous functions intracellularly and extracellularly. However, because of the availability of well studied anticonvulsant drugs, Mg has not been tested widely in the treatment of epileptic seizures. METHODS: A retrospective chart review of 22 cases of drug resistant epilepsy, where a trial of empiric oral Mg supplementation (mainly in the form of Mg-oxide) was conducted. RESULTS: Oral Mg supplementation was associated with a significant decrease in the number of seizure days per month, from 15.3 ± 13.2 (mean ± SD) to 10.2 ± 12.6 at first follow up (3-6 months, p=0.021), and to 7.8 ± 10.0 seizure days/month at second follow up (6-12 months, p=0.004). Thirty-six percent had a response rate of 75% or greater at second follow-up. Two patients reported seizure freedom. Most patients were well maintained on MgO 420 mg twice a day, or in 2 cases, Mg Lactate, without significant adverse effects, the most frequent being diarrhea (4/22). DISCUSSION: These results suggest that oral Mg supplementation may prove to be a worthwhile adjunctive medication in treating drug intractable epilepsy. CONCLUSIONS: A prospective, double-blinded, placebo controlled study is warranted to evaluate the potential of Mg for the treatment of drug-resistant seizures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".