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Record W3026637300 · doi:10.1016/s0140-6736(20)30543-2

Variations between women and men in risk factors, treatments, cardiovascular disease incidence, and death in 27 high-income, middle-income, and low-income countries (PURE): a prospective cohort study

2020· article· en· W3026637300 on OpenAlexaff
Marjan Walli-Attaei, Philip Joseph, Annika Rosengren, Clara K Chow, Sumathy Rangarajan, Scott A. Lear, Khalid F. AlHabib, Kairat Davletov, Antonio Dans, Fernando Laņas, Karen Yeates, Paul Poirier, Koon Teo, Ahmad Bahonar, Jephat Chifamba, Rafael Díaz, Joanna Didkowska, Vilma Irazola, Rosnah Ismail, Manmeet Kaur, Rasha Khatib, Xiaoyun Liu, Marta Mańczuk, J. Jaime Miranda, Aytekin Oğuz, Maritza Pérez-Mayorga, Andrzej Szuba, Lungiswa Tsolekile, Ravi Prasad Varma, Afzalhussein Yusufali, Rita Yusuf, Wei Li, Sonia S. Anand, Salim Yusuf

Bibliographic record

VenueThe Lancet · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecMcMaster UniversityQueen's UniversitySimon Fraser UniversityHamilton Health SciencesPopulation Health Research Institute
Fundersnot available
KeywordsMedicinePopulationIncidence (geometry)DiseaseProspective cohort studyEpidemiologyPsychological interventionGerontologyCohort studyRisk factorDemographyCohortEnvironmental healthInternal medicine

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)0.001
metaresearch head score (Gemma)0.002
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.283
Teacher spread0.260 · 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

Citations335
Published2020
Admission routes1
Has abstractno

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