MétaCan
Menu
Back to cohort
Record W2330293594 · doi:10.1139/cjpp-2012-0295

Redox-inflammatory synergy in the metabolic syndrome

2013· review· en· W2330293594 on OpenAlexafffundvenue
Sean Bryan, Boran Baregzay, Drew Spicer, Pawan K. Singal, Neelam Khaper

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2013
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsNOSM UniversitySt. Boniface HospitalLakehead University
FundersCanadian Institutes of Health Research
KeywordsMetabolic syndromeInsulin resistanceOxidative stressInflammationDyslipidemiaType 2 diabetesMedicineType 2 Diabetes MellitusDiabetes mellitusPathogenesisDiseaseBioinformaticsEndocrinologyInternal medicineBiologyImmunology

Abstract

fetched live from OpenAlex

Metabolic syndrome (MetS) comprises interrelated disease states including obesity, insulin resistance and type 2 diabetes (T2DM), dyslipidemia, and hypertension. Essential to normal physiological function, and yet massively damaging in excess, oxidative stress and inflammation are pivotal common threads among the pathologies of MetS. Increasing evidence indicates that redox and inflammatory dysregulation parallels the syndrome's physiological, biochemical, and anthropometric features, leading many to consider the pro-oxidative, pro-inflammatory milieu an unofficial criterion in itself. Left unchecked, cross-promotion of oxidative stress and inflammation creates a feed-forward cycle that can initiate and advance disease progression. Such redox-inflammatory integration is evident in the pathogenesis of obesity, insulin resistance and T2DM, atherogenic dyslipidemia, and hypertension, and is thus hypothesized to be the "common soil" from which they develop. The present review highlights the synergistic contributions of redox-inflammatory processes to each of the components of the MetS.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.310
Teacher spread0.285 · 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

Citations79
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Physiology and PharmacologySame topicAdipokines, Inflammation, and Metabolic DiseasesFrench-language works237,207