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Record W4286790088 · doi:10.1002/ehf2.14076

Impact Analysis of Heart Failure Across European Countries: An ESC-HFA Position Paper

2022· review· en· W4286790088 on OpenAlexaff
Giuseppe Rosano, Petar Seferović, Gianluigi Savarese, Ilaria Spoletini, Yu. M. Lopatin, Finn Gustafsson, Antoni Bayés‐Genís, Tiny Jaarsma, Magdy Abdelhamid, Arantxa González, Massimo Piepoli, Carlo G. Tocchetti, Arsen Ristić, Ewa A. Jankowska, Brenda Moura, Loreena Hill, Gerasimos Filippatos, Marco Metra, Davor Miličić, Thomas Thum, Ovidiu Chioncel, Tuvia Ben Gal, Lars H. Lund, Dimitrios Farmakis, Wilfried Müllens, Stamatis Adamopoulos, Michael Böhm, Anna Norhammar, Andreas Bollmann, Amitava Banerjee, Aldo P. Maggioni, Adriaan A. Voors, Alain Cohen Solal, Andrew J.S. Coats

Bibliographic record

VenueESC Heart Failure · 2022
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsHeart failureMedicinePosition (finance)Position paperCardiologyInternal medicineEconomicsPathology

Abstract

fetched live from OpenAlex

Heart failure (HF) is a long-term clinical syndrome, with increasing prevalence and considerable healthcare costs that are further expected to increase dramatically. Despite significant advances in therapy and prevention, mortality and morbidity remain high and quality of life poor. Epidemiological data, that is, prevalence, incidence, mortality, and morbidity, show geographical variations across the European countries, depending on differences in aetiology, clinical characteristics, and treatment. However, data on the prevalence of the disease are scarce, as are those on quality of life. For these reasons, the ESC-HFA has developed a position paper to comprehensively assess our understanding of the burden of HF in Europe, in order to guide future policies for this syndrome. This manuscript will discuss the available epidemiological data on HF prevalence, outcomes, and human costs-in terms of quality of life-in European countries.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.364
Teacher spread0.333 · 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

Citations105
Published2022
Admission routes1
Has abstractyes

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