Over-the-counter anti-oxidant therapies for use in multiple sclerosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Anti-oxidant compounds that are found in over-the-counter (OTC) supplements and foods are gaining interest as treatments for multiple sclerosis (MS). They are widely used by patients, sometimes without a clear evidence base. OBJECTIVE: We conducted a systematic review of animal and clinical research to determine the evidence for the benefits of OTC anti-oxidants in MS. METHODS: Using predefined criteria, we searched key databases. Two authors scrutinized all studies against inclusion/exclusion criteria, assessed study risk-of-bias and extracted results. RESULTS: Of the 3507 titles, 145 met criteria and included compounds, α(alpha)-lipoic acid (ALA), anti-oxidant vitamins, Ginkgo biloba, quercetin, resveratrol and epigallocatechin-3-gallate (ECGC). The strongest evidence to support OTC anti-oxidants was for compounds EGCG and ALA in animal models; both consistently showed anti-inflammatory/anti-oxidant effects and reduced neurological impairment. Only vitamin E, Ginkgo biloba and ALA were examined for efficacy in pilot clinical trials with either conflicting evidence or evidence of no benefit. CONCLUSION: OTC anti-oxidants EGCG and ALA show the most consistent benefit, however only in preclinical studies. There is no evidence that they alter MS relapses or progression. Future work should focus on testing more of these therapies for clinical efficacy before recommending them to MS patients.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".