MétaCan
Menu
Back to cohort
Record W2909216997 · doi:10.1161/circgen.118.002335

Variation in Serum PCSK9 (Proprotein Convertase Subtilisin/Kexin Type 9), Cardiovascular Disease Risk, and an Investigation of Potential Unanticipated Effects of PCSK9 Inhibition

2019· letter· en· W2909216997 on OpenAlex
Ben Brumpton, Lars G. Fritsche, Jie Zheng, Jonas B. Nielsen, Maria Mannila, Ida Surakka, Humaira Rasheed, Gunnhild Åberge Vie, Sarah E. Graham, Maiken E. Gabrielsen, Lars Erik Laugsand, Pål Aukrust, Lars J. Vatten, Jan Kristian Damås, Thor Ueland, Imre Janszky, John‐Anker Zwart, Ferdinand M. van’t Hooft, Nabil G. Seidah, Kristian Hveem, Cristen J. Willer, George Davey Smith, Bjørn Olav Åsvold

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCirculation Genomic and Precision Medicine · 2019
Typeletter
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsInstitut de Readaptation Gingras Lindsay de Montreal
FundersNational Heart, Lung, and Blood InstituteMedical Research CouncilCanadian Institutes of Health Research
KeywordsPCSK9KexinProprotein convertaseSubtilisinMedicineInternal medicineEndocrinologyCholesterolLDL receptorChemistryLipoproteinBiochemistryEnzyme

Abstract

fetched live from OpenAlex

PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitors reduce serum LDL (low-density lipoprotein) cholesterol (LDL-C) by increasing uptake in the liver. Although some long-term trials have evaluated their safety, broad investigations of outcomes over the lifetime, leveraging genetic variation in serum PCSK9, have seldomly been conducted. We investigated effects of these variants on a range of outcomes to explore unanticipated effects of long-term PCSK9 inhibition.

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.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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