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Record W3111059202 · doi:10.1097/med.0000000000000594

Best practice for treating dyslipidaemia in patients with diabetes based on current international guidelines

2020· review· en· W3111059202 on OpenAlexaboutno aff
N. Lan, Kharis Burns, Damon A. Bell, Gerald F. Watts

Bibliographic record

VenueCurrent Opinion in Endocrinology Diabetes and Obesity · 2020
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
Fundersnot available
KeywordsEzetimibeMedicineDiabetes mellitusStatinInternal medicineContext (archaeology)Type 2 diabetesIntensive care medicineEndocrinology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Dyslipidaemia is a major modifiable risk factor for atherosclerotic cardiovascular disease (ASCVD) in type 2 diabetes. We provide an in-context overview of recent trials of lipid-lowering pharmacotherapies and of recommendations from international guidelines for managing dyslipidaemia in patients with diabetes. RECENT FINDINGS: Clinical trials have demonstrated that patients with diabetes derive greater benefits from ezetimibe and proprotein convertase subtilisin-kexin type 9 inhibitors owing to the higher absolute ASCVD risk compared with patients without diabetes. Pure eicosapentaenoic acid ethyl ester therapy should be considered in high risk patients with diabetes and hypertriglyceridaemia who have well controlled low-density lipoprotein cholesterol on statin therapy. International guidelines from USA, Canada and Europe have been updated to support a more intensive approach to treating dyslipidaemia in diabetes. SUMMARY: Dyslipidaemia should be identified and treated intensively as part of overall diabetes management to reduce ASCVD risk. Although lifestyle modifications and statin therapy remain the cornerstone of management, add-on therapies should be strongly considered depending on the absolute risk of ASCVD and the degree of dyslipidaemia.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.384
Teacher spread0.324 · 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 teacher head, not a consensus.

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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

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