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Record W2097123269

Dyslipidemia in Type 2 Diabetes: What's the Role of Combination Drugs?

2005· article· en· W2097123269 on OpenAlexaboutno aff
Raymond Fung, Dominic S. Ng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
Fundersnot available
KeywordsInternal medicineEndocrinologyVery low-density lipoproteinDyslipidemiaDiabetes mellitusMedicineType 2 diabetesCholesterolLipoproteinMetabolic syndromeTriglyceride
DOInot available

Abstract

fetched live from OpenAlex

Diabetes is a strong risk factor for cardiovascular (CV) events and death. It is associated with a two-to-four-fold increased risk of cardiovascular disease (CVD), causing up to 80% of deaths in people with diabetes. The control of blood glucose, blood pressure and lipids is among the 2003 Canadian Diabetes Guidelines’ highest-priority mandated measures for vascular protection. Those patients who are young, with shorter duration of diabetes, no other CVD risk factors and no other complications of diabetes may be considered at moderate risk (Table 1). The most common lipid abnormality in diabetes is high triglyceride (TG) and low highdensity lipoprotein (HDL) cholesterol (HDL-C) levels. Although low-density lipoprotein (LDL) cholesterol (LDL-C) may not be elevated, an increased proportion of LDL has been found to be of the more pro-atherogenic, small, dense variety. This lipid profile contributes to the high prevalence of metabolic syndrome in patients with Type 2 diabetes and shares many of its pathophysiological features. The primary abnormality of metabolic syndrome is the liver’s over-production of very low-density lipoprotein (VLDL), the major TG-carrying particles and precursor of the more cholesterolenriched LDL. The breakdown of TG by the enzyme called lipoprotein lipase may also be partially impaired, contributing to the high level of TG. The VLDL in the circulation interacts with HDL and LDL and exchanges their lipid contents through an enzyme called cholesterol-ester transfer protein. Excess TG in VLDL is transferred to both HDL and LDL in exchange for cholesterol; as a result, the excess TG-in HDL are broken down by an enzyme called hepatic lipase (HL), resulting in accelerated clearance of HDL and a low level of HDL-C. The excess TG in LDL is also broken down by HL, transforming them into small, dense LDL. Small, dense LDL is cleared more slowly by the liver through the LDL receptor, more readily enters arterial walls, induces more endothelial dysfunction and is more susceptible to oxidation. This vicious cycle creates the atherogenic triad of small, dense LDL, high TG and low HDL. Three classes of medications are commonly used to treat dyslipidemia in patients with diabetes: HMG-CoA reductase inhibitors (statins), fibric acid derivatives and niacin.

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)0.003
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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
GenreOther

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
Published2005
Admission routes1
Has abstractyes

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