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Record W2089354656 · doi:10.1038/clpt.2009.311

Obesity, Inflammation, and Cardiovascular Risk

2010· review· en· W2089354656 on OpenAlexaff
Patrick Mathieu, Isabelle Lemieux, J-P Després

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2010
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAdipose tissueContext (archaeology)InflammationObesityDiseaseMedicineBioinformaticsEpidemiologyMetabolic syndromeDysfunctional familyInternal medicineEndocrinologyBiology

Abstract

fetched live from OpenAlex

Obesity, a highly prevalent condition, is heterogeneous with regard to its impact on cardiovascular disease (CVD) risk. Epidemiological observations and metabolic investigations have consistently demonstrated that the accumulation of excess visceral fat is related to an increased risk of CVD as well as several metabolic and inflammatory perturbations. In the past decade, data from several studies have served to emphasize that atherosclerosis has an inflammatory component that may contribute to several key pathophysiological processes. Study data have also highlighted the finding that the expanded visceral fat is infiltrated by macrophages that conduct "cross-talk" with adipose tissue through several significant mechanisms. In this review, we provide, in the context of CVD risk, an up-to-date account of the complex interactions that occur between a dysfunctional adipose tissue phenotype and inflammation.

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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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)0.0040.003

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 head0.092
GPT teacher head0.427
Teacher spread0.336 · 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

Citations421
Published2010
Admission routes1
Has abstractyes

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