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C‐Reactive Protein and the Metabolic Syndrome: Useful Addition to the Cardiovascular Risk Profile?

2006· review· en· W2113474990 on OpenAlexaff
Paul E. Szmitko, Subodh Verma

Bibliographic record

VenueJournal of the CardioMetabolic Syndrome · 2006
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of TorontoToronto General HospitalSt. Michael's Hospital
Fundersnot available
KeywordsMedicineC-reactive proteinMetabolic syndromeDiabetes mellitusFramingham Risk ScoreDiseaseInternal medicineStatinAdiponectinAtherosclerotic cardiovascular diseaseCholesterolNational Cholesterol Education ProgramInflammationBioinformaticsEndocrinologyInsulin resistance

Abstract

fetched live from OpenAlex

The inflammatory marker C-reactive protein has emerged as a powerful independent predictor of cardiovascular disease risk. C-reactive protein may also be a mediator of inflammatory processes such as atherosclerosis development and progression, and it appears to be useful in identifying patients with, or at risk for developing, diabetes mellitus and the metabolic syndrome. In the clinical practice setting, measurement of C-reactive protein levels can add information to help guide management decisions in persons who are at intermediate risk based on Framingham risk scores, who have preexisting cardiovascular disease, or who exhibit components of the metabolic syndrome. A large ongoing trial is investigating whether statin therapy will decrease the risk of cardiovascular disease in patients with elevated levels of C-reactive protein and low-to-normal levels of low-density lipoprotein cholesterol.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.005

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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