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Record W4406980068 · doi:10.1016/j.jacadv.2024.101582

Beta-Blocker Therapy After Myocardial Infarction

2025· review· en· W4406980068 on OpenAlexaff
Patrícia Miranda, Danijela Gašević, Caroline Trin, Dion Stub, Sophia Zoungas, David M. Kaye, Zhomart Orman, Amminadab L Eliakundu, Stella Talic

Bibliographic record

VenueJACC Advances · 2025
Typereview
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsMyocardial infarctionBETA (programming language)MedicineCardiologyBeta blockerInternal medicineHeart failureComputer science

Abstract

fetched live from OpenAlex

Historical data strongly supported the benefits of beta-blocker therapy following a myocardial infarction (MI) for its efficacy in reducing mortality and morbidity. However, in the context of the progressive evolution of treatment strategies for MI patients, the apparent benefit of beta-blocker therapy is becoming less clear. In particular, its effectiveness in patients with preserved left ventricular ejection fraction is currently being challenged. Consequently, contemporary guidelines are now varying in their recommendations regarding the role of beta-blocker therapy in post-MI patients. This review aims to summarize and compare the largest and most influential studies from the prereperfusion era to modern practice regarding different health outcomes while highlighting the need for further research to clarify beta-blocker therapy's place in contemporary post-MI management.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.396
Teacher spread0.356 · 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

Citations17
Published2025
Admission routes1
Has abstractyes

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