‘Functional mushrooms saved my daughter’s Life’: A preliminary mixed methods survey study of Reishi (Ganoderma lucidum) and lion’s mane (Hericium erinaceus) for epilepsy
Bibliographic record
Abstract
A significant proportion of epilepsy patients are resistant to conventional treatments. Functional mushrooms such as Reishi ( Ganoderma lucidum ) and Lion’s Mane ( Hericium erinaceus ) have recognized health benefits, including neuroprotective and anti-seizure effects. This mixed-methods study investigates the use of these mushrooms as adjunctive therapies for epilepsy among 19 participants, including caregivers on behalf of a minor. The majority (n = 14) met criteria for drug-resistant epilepsy (DRE). Participants completed surveys and structured interviews addressing functional mushroom use, quality of life (QoL), sleep quality, health outcomes, seizure activity, and caregiver burden. Qualitative data underwent thematic content analysis. Compared with the general population, participants utilizing functional mushrooms noted comparable scores in physical health, psychological health, social relationships, and environmental health. Self-report responses indicated improvements in seizure control, including reduced seizure frequency and severity, cognitive and developmental outcomes, and overall quality of life since starting use of functional mushrooms. Caregivers emphasized both benefits and ongoing challenges in managing epilepsy. Functional mushrooms are a sustainable and widely available resource, and the findings presented here suggest they could complement existing treatments for epilepsy, particularly in DRE, though further clinical trials are needed to establish efficacy, optimize dosing, and clarify mechanisms of action. • Functional mushrooms (Reishi and Lion’s Mane) were associated with reduced seizure severity and frequency in individuals with epilepsy, including drug-resistant cases. • Participants reported improvements in quality of life, cognitive functioning, and developmental outcomes, aligning with both quantitative and qualitative findings. • Caregivers noted reduced stress and burden when seizure severity decreased, pointing to broader family benefits. • A subset of participants with drug-resistant epilepsy showed signs of clinically meaningful improvement, with some likely to achieve seizure freedom. • Findings highlight the need for larger, controlled clinical trials to establish efficacy, dosing, and safety of functional mushrooms as adjunctive epilepsy treatments.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".