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Record W4411272824 · doi:10.1093/eurjpc/zwaf342

Rationale for the routine screening of Lipoprotein(a) in cardiovascular risk assessment

2025· article· en· W4411272824 on OpenAlexaffabout
Iulia Iatan, Marlys L. Koschinsky, Logan Trenaman, Wei Zhang, George Thanassoulis, Liam R. Brunham, G.B. John Mancini, Gordon A. Francis

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2025
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsProvidence Health CareCentre for Advancing Health OutcomesWestern UniversityMcGill University Health CentreSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineLipoprotein(a)Risk assessmentAtherosclerotic cardiovascular diseaseLdl cholesterolIntensive care medicineLipoproteinInternal medicineCholesterolDisease

Abstract

fetched live from OpenAlex

Lipoprotein(a) [Lp(a)] is a lipid particle identified by Mendelian randomization studies to be causally associated with the development of atherosclerotic cardiovascular disease and aortic stenosis, across ethnicities. The risk of cardiovascular disease with markedly elevated Lp(a) is equal to that of untreated familial hypercholesterolemia, and yet, up until now, there has been hesitancy in measuring Lp(a) as a routine part of cardiovascular risk assessment. Screening of Lp(a) level in all individuals is now recommended in the European and Canadian Lipid Guidelines and by the National Lipid Association. This review assesses how well measurement of Lp(a) meets accepted criteria for population screening of an analyte, based on established principles used for the selection of a new candidate for inclusion in screening programs. Lp(a) meets the majority of recommended principles for a routine population screening test, based on health, societal, and cost considerations. Incorporating Lp(a) into global assessment and management of cardiovascular risk will result in savings to health care systems, reinforce recommendations from growing numbers of clinical guidelines and consensus statements, and increase implementation of proactive preventive medicine.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.295
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2025
Admission routes2
Has abstractyes

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