MétaCan
Menu
Back to cohort
Record W4210277682 · doi:10.1093/eurjpc/zwab147

Preventing heart failure: a position paper of the Heart Failure Association in collaboration with the European Association of Preventive Cardiology

2021· review· en· W4210277682 on OpenAlexaff
Massimo Piepoli, Marianna Adamo, Andrea Barison, Reinaldo B. Bestetti, Jan Biegus, Michael Böhm, Javed Butler, Jonathan R. Carapetis, Claudio Ceconi, Ovidiu Chioncel, Andrew J.S. Coats, María G. Crespo‐Leiro, Giovanni de Simone, Heinz Drexel, Michele Emdin, Dimitros Farmakis, Martin Halle, Stéphane Heymans, Tiny Jaarsma, Ewa A. Jankowska, Mitja Lainščak, Carolyn S.P. Lam, Maja‐Lisa Løchen, Yu. M. Lopatin, Aldo P. Maggioni, Benedetta Matrone, Marco Metra, Katharine Noonan, Ileana L. Piña, Eva Prescott, Giuseppe Rosano, Petar Seferović, Karen Sliwa, Simon Stewart, Alicia Uijl, Ilonca Vaartjes, Roel Vermeulen, W. M. Monique Verschuren, Maurizio Volterrani, Stephan von Haehling, Arno W. Hoes

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2021
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
FundersMinistero della Salute
KeywordsMedicineHeart failurePosition paperIntensive care medicinePreventive healthcarePsychological interventionPopulationMedical emergencyEnvironmental healthCardiologyPublic healthPathologyNursing

Abstract

fetched live from OpenAlex

The heart failure epidemic is growing and its prevention, in order to reduce associated hospital readmission rates and its clinical and economic burden, is a key issue in modern cardiovascular medicine. The present consensus document aims to provide practical evidence-based information to support the implementation of effective preventive measures. After reviewing the most common risk factors, an overview of the population attributable risks in different continents is presented, to identify potentially effective opportunities for prevention and to inform preventive strategies. Finally, potential interventions that have been proposed and have been shown to be effective in preventing HF are listed.

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 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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.274
Teacher spread0.262 · 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 teacher head, not a consensus.

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

Citations37
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Preventive CardiologySame topicHeart Failure Treatment and ManagementFrench-language works237,207