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Record W2789648792 · doi:10.1097/hco.0000000000000518

An update on trials of novel lipid-lowering drugs

2018· review· en· W2789648792 on OpenAlexaff
Anthony S. Wierzbicki, Tim Reynolds, Adie Viljoen

Bibliographic record

VenueCurrent Opinion in Cardiology · 2018
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsSt. Thomas Hospital
FundersAstraZeneca
KeywordsMedicineEvolocumabCanakinumabPCSK9KexinCholesterylester transfer proteinProprotein convertasePopulationCholesterolInternal medicinePharmacologyCoronary artery diseaseClinical trialStatinLipoproteinDiseaseLDL receptor

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: A number of novel trials have assessed the efficacy of new lipid-lowering therapies in cardiovascular disease (CVD). RECENT FINDINGS: Proprotein convertase subtilisin kexin-9 inhibitors reduce low-density lipoprotein cholesterol (LDL-C) by 50-55%. A CVD outcome trial in patients with acute coronary syndromes with evolocumab achieved a LDL-C of 0.8 mmol/l (31 mg/dl) and a 20% relative risk reduction in CVD events in 2.2 years. Cholesterol ester transfer protein inhibitors raise high-density lipoprotein cholesterol and can lower LDL-C. Anacetrapib reduced coronary artery disease events by 7%, but not wider composite CVD outcomes, in a population with chronic CVD with pretreatment LDL-C of 1.6 mmol/l (62 mg/dl). The conflicting outcomes of cholesterol ester transfer protein inhibitor trials means these compounds are not being developed further. Trials using lipid drugs targeting inflammation have previously been generally unsuccessful, but recent data on the interleukin-1B receptor antagonist canakinumab has proven the concept of intervention on inflammation in atherosclerosis by showing a reduction in acute coronary interventions, but at the predictable cost of increased infections. SUMMARY: Despite the success of proprotein convertase subtilisin kexin-9 inhibition, the ability to achieve low LDL-C with off-patent medications and the costs of novel therapies will limit their use even in high-risk patients and confine them to the highest-risk sub-groups of patients.

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)0.006
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 score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.231
GPT teacher head0.474
Teacher spread0.243 · 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
Published2018
Admission routes1
Has abstractyes

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