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Record W3206535466 · doi:10.1080/21678421.2021.1990346

ALSUntangled #63: ketogenic diets

2021· review· en· W3206535466 on OpenAlexaff
Richard Bedlack, Paul E. Barkhaus, Benjamin Barnes, Morgan Beauchamp, Tulio E. Bertorini, Mark B. Bromberg, Gregory T. Carter, Vinay Chaudry, Merit Cudkowicz, Jackson Ce, G. N. Levitsky, Isaac Lund, Christopher McDermott, Steven Novella, Natasha J. Olby, Lyle W. Ostrow, Gary L. Pattee, Terry Heiman‐Patterson, Dylan Ratner, Kristiana Salmon, Susan Steves, Mark Terrelonge, Paul Wicks, Anne‐Marie Wills

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2021
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcGill University Health Centre
FundersALS Association
KeywordsKetogenic dietExcitotoxicityNeuroinflammationOxidative stressMedicineNeuroscienceIntensive care medicinePsychologyEpilepsyInternal medicine

Abstract

fetched live from OpenAlex

ALSUntangled reviews alternative and off label treatments with a goal of helping patients make more informed decisions about them. Here we review ketogenic diets. We shows that these have plausible mechanisms, including augmenting cellular energy balance and reducing excitotoxicity, neuroinflammation and oxidative stress. We review a mouse model study, anecdotal reports and trials in ALS and other diseases. We conclude that there is yet not enough data to recommend ketogenic diets for patients with ALS, especially in light of the many side effects these can have.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.005

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 head0.091
GPT teacher head0.315
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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