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Record W4413283272 · doi:10.3390/jpm15080385

The Exchange Breathing Method for Seizure Intervention: A Historical and Scientific Review of Epilepsy and Its Evolving Therapeutic Paradigms

2025· review· en· W4413283272 on OpenAlexaff
Frederick Robert Carrick, Pamela Daniels, Stephen R. Pelletier, Sofia Prysmakova, Ahmed Hankir, Mahera Abdulrahman, Nouf Alrumaihi

Bibliographic record

VenueJournal of Personalized Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsEpilepsyIntervention (counseling)MedicineBreathingNeurosciencePsychologyAnesthesiaPsychiatry

Abstract

fetched live from OpenAlex

Epilepsy is a complex and ancient neurological disorder affecting approximately 50 million individuals globally. Despite significant advancements in pharmacological treatments, surgical procedures, and neurostimulation techniques, a substantial subset of patients remains pharmacoresistant or experiences intolerable side effects, highlighting the need for novel, safe, and effective interventions. In this review, we examine a promising non-invasive technique known as the Exchange Breathing Method (EBM), developed through the observations of Gemma Herbertson, a British mother who discovered that exhaling gently into her son's nostrils could consistently interrupt ongoing seizures. The EBM has since gained anecdotal support from a growing international community reporting similar positive outcomes. This paper situates the EBM within the broader historical and clinical context of epilepsy treatment, tracing its evolution from ancient practices to modern therapeutic strategies. We explore the neurophysiological mechanisms that may underlie the EBM, particularly its interaction with autonomic and respiratory pathways implicated in seizure modulation. By integrating emerging grassroots data with current scientific knowledge, this review proposes a rationale for further empirical investigation into the EBM and its potential role in the personalized, emergency management of epilepsy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.713
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.098
GPT teacher head0.449
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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