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Record W4211152466 · doi:10.1161/cir.0000000000000624

2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines

2019· review· en· W4211152466 on OpenAlexfundno aff
Scott M. Grundy, Neil J. Stone, Alison Bailey, Craig Beam, Kim K. Birtcher, Roger S. Blumenthal, Lynne T. Braun, Sarah D. de Ferranti, Joseph Faiella-Tommasino, Daniel E. Forman, Ronald Goldberg, Paul A. Heidenreich, Mark A. Hlatky, Daniel W. Jones, Donald M. Lloyd‐Jones, Nuria Lopez-Pajares, Chiadi E. Ndumele, Carl E. Orringer, Carmen Peralta, Joseph J. Saseen, Sidney C. Smith, Laurence Sperling, Salim S. Virani, Joseph Yeboah

Bibliographic record

VenueCirculation · 2019
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
FundersCenter for Innovations in Quality, Effectiveness and SafetyUniversity of California, San FranciscoUniversity of North Carolina at Chapel HillPublic Health Agency of CanadaU.S. Department of Veterans AffairsRush UniversityUniversity of PittsburghOffice of ScienceJohns Hopkins UniversityAmerican Pharmacists AssociationNorthwestern UniversityEmory UniversityTemple UniversityAmerican Diabetes AssociationAmerican Heart AssociationUniversity of Miami
KeywordsMedicineGuidelineClinical PracticeTask forceInternal medicineFamily medicinePathologyPolitical sciencePublic administration

Abstract

fetched live from OpenAlex

cardiovascular disease cholesterol, LDL-cholesterol diabetes mellitus drug therapy hydroxymethylglutaryl-CoA reductase inhibitors/statins hypercholesterolemia lipids patient compliance primary prevention risk assessment risk reduction discussion risk treatment discussion, secondary prevention ezetimibe proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9) inhibitors

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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0160.012

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 head0.084
GPT teacher head0.410
Teacher spread0.326 · 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

Citations905
Published2019
Admission routes1
Has abstractyes

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