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Record W4388364315 · doi:10.1016/j.ibneur.2023.08.316

KETONE ESTERS PARTIALLY RESCUE MITOCHONDRIAL BIOENERGETICS AFTER ACUTE CERVICAL SCI IN RATS: A TIME-COURSE STUDY.

2023· article· en· W4388364315 on OpenAlexaff
Oscar Seira, Hyojoon Park, Jie Liu, Michelle Poovathukaran, Kieran Clarke, Robert Boushel, Wolfram Tetzlaff

Bibliographic record

VenueIBRO Neuroscience Reports · 2023
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
Fundersnot available
KeywordsBioenergeticsKetoneChemistryMedicineMitochondrionBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Spinal Cord Injury (SCI) pathophysiology can be attributed to either primary physical injury or secondary injury cascades. Secondary Injury cascades lead to dysregulated metabolism and energetic deficits, which are directly linked to mitochondrial bioenergetics dysfunction. Neuroprotection and reduced oxidative stress are associated with improved cellular bioenergetics. In this regard, ketosis after traumatic brain injury (TBI), or after SCI has already been shown to improve secondary neuropathology by decreasing oxidative stress, increasing antioxidants, reducing inflammation, and improving mitochondrial bioenergetics.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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