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Record W4399555359 · doi:10.1016/j.jchf.2024.04.025

Where Are We With Treatment and Prevention of Heart Failure in Patients Post–Myocardial Infarction?

2024· article· en· W4399555359 on OpenAlex
Jaclyn Carberry, Guillaume Marquis‐Gravel, Eileen O’Meara, Kieran F. Docherty

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.

Bibliographic record

VenueJACC Heart Failure · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineMyocardial infarctionHeart failureInternal medicineCardiologyAdverse effectRisk stratificationNeprilysinIntensive care medicine

Abstract

fetched live from OpenAlex

As a result of the widespread use of reperfusion therapies and secondary prevention over the last 30 years, there has been a dramatic reduction in the risk of mortality and development of heart failure (HF) following acute myocardial infarction (MI). Despite this, the development of chronic HF remains a common occurrence in the days, months, and years following MI. Neurohormonal inhibition remains the mainstay of pharmacologic prevention of HF following MI, with recent trials showing an additive benefit of a neprilysin inhibitor or a sodium glucose co-transporter 2 inhibitor in reducing the risk of development of HF but no significant effect on mortality. Novel imaging tools may help refine risk stratification in high-risk patients and allow greater targeting of preventative therapies in patients most likely to benefit. Research is ongoing into novel therapies aiming to minimize the degree of myocardial damage and prevention of progressive adverse remodeling following MI.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.205
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.261
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