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Record W3090997876 · doi:10.1016/s0140-6736(20)31964-4

Plasma ACE2 and risk of death or cardiometabolic diseases: a case-cohort analysis

2020· article· en· W3090997876 on OpenAlexafffund
Sukrit Narula, Salim Yusuf, Michael Chong, Chinthanie Ramasundarahettige, Sumathy Rangarajan, Shrikant I. Bangdiwala, Martin van Eikels, Kirsten Leineweber, Annie M. Wu, Marie Pigeyre, Guillaume Paré

Bibliographic record

VenueThe Lancet · 2020
Typearticle
Languageen
FieldMedicine
TopicRenin-Angiotensin System Studies
Canadian institutionsThrombosis and Atherosclerosis Research InstituteImpactHamilton Health SciencesPopulation Health Research InstituteMcMaster University
FundersMedical Research CouncilIndependent University, BangladeshServierCanadian Institutes of Health ResearchAFA FörsäkringForskningsrådet för Arbetsliv och SocialvetenskapSvenska Forskningsrådet FormasNational Research FoundationNorth-West UniversityPublic Health Agency of CanadaUniversidad de La FronteraGlaxoSmithKlinePublic Health AgencySanofiVetenskapsrådetHeart and Stroke Foundation of CanadaOntario Ministry of Health and Long-Term CareDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)South Africa Netherlands research Programme on Alternatives in DevelopmentFaculty of Community and Health Sciences, University of the Western CapeAstraZeneca
KeywordsMedicineHazard ratioInternal medicineProspective cohort studyMyocardial infarctionBody mass indexDiabetes mellitusCohort studyHeart failureCohortEpidemiologyEndocrinologyCardiologyConfidence interval

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.043
GPT teacher head0.296
Teacher spread0.253 · 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 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

Citations169
Published2020
Admission routes2
Has abstractno

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