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Record W4321371023 · doi:10.3390/sclerosis1010003

Multiple Sclerosis and Sodium Toxicity: Controversy and Future Directions for Low-Salt Interventions

2023· article· en· W4321371023 on OpenAlexaff
Ronald B. Brown

Bibliographic record

VenueSclerosis · 2023
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMultiple sclerosisSodiumMedicineDiseaseToxicityPhysiologyInternal medicineImmunologyEndocrinologyChemistry

Abstract

fetched live from OpenAlex

Salt intake is associated with multiple sclerosis; however, controversial findings that challenge this association rely primarily on methods that do not measure total sodium storage within the body, such as food surveys and urinary sodium excretion. In contrast, tissue sodium concentrations measured with sodium MRI confirm high sodium levels in multiple sclerosis, suggesting a role for sodium toxicity as a risk factor for the disease. Research on demyelination in the central nervous system has identified myelin phase transitions associated with increased salinity, which cause structural instabilities of myelin sheaths and add further evidence implicating sodium toxicity as a causative factor in multiple sclerosis. Inflammatory and immune responses in multiple sclerosis are also related to high sodium intake. In addition, salt is a potential mediating factor associating multiple sclerosis with comorbidities, including systemic lupus erythematosus, rheumatic arthritis, inflammatory bowel disease, and cardiovascular disease. Current confusion exists over classifying dietary sodium intake levels as low, normal, and high, and questions remain over levels of sodium restriction necessary for disease prevention. To reduce multiple sclerosis symptoms and prevent disease progression in patients, future research should investigate low-salt interventions with levels of sodium intake associated with ancestral hunter-gatherer tribes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.075
GPT teacher head0.304
Teacher spread0.228 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes1
Has abstractyes

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