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Record W3196464088 · doi:10.1016/s0140-6736(21)01827-4

Fixed-dose combination therapies with and without aspirin for primary prevention of cardiovascular disease: an individual participant data meta-analysis

2021· article· en· W3196464088 on OpenAlexaff
Philip Joseph, Gholamreza Roshandel, Peggy Gao, Prem Pais, Eva Lonn, Denis Xavier, Álvaro Avezum, Jun Zhu, Lisheng Liu, Karen Sliwa, Habib Gamra, Shrikant I. Bangdiwala, Koon Teo, Rafael Díaz, Antonio Dans, Patricio López‐Jaramillo, Dorairaj Prabhakaran, José M. Castellano, Valentı́n Fuster, Anthony Rodgers, Mark D. Huffman, Jackie Bosch, Gilles R. Dagenais, Reza Malekzadeh, Salim Yusuf

Bibliographic record

VenueThe Lancet · 2021
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsInstitut Universitaire de Cardiologie et de Pneumologie de QuébecPopulation Health Research InstituteMcMaster UniversityUniversité LavalHamilton Health Sciences
Fundersnot available
KeywordsMedicineAspirinStroke (engine)PopulationInternal medicineMyocardial infarctionHazard ratioPlaceboProportional hazards modelRandomized controlled trialStatinFixed-dose combinationPhysical therapyConfidence 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)0.019
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.034
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0170.054
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.365
GPT teacher head0.394
Teacher spread0.029 · 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.

Study designMeta-analysis
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

Citations148
Published2021
Admission routes1
Has abstractno

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