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Record W3032924626 · doi:10.37616/2212-5043.1040

Heart Failure In The Middle East Arab Countries: Current And Future Perspectives

2020· review· en· W3032924626 on OpenAlexaff
Abdelfatah Elasfar, Waleed AlHabeeb, Salma Elasfar

Bibliographic record

VenueJournal of the Saudi Heart Association · 2020
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHeart failureMedicineHeart transplantationCoronary artery diseaseDeveloping countryEjection fractionPopulationEpidemiologyDeveloped countryHeart diseaseIntensive care medicineCardiologyInternal medicineEconomic growthEnvironmental health

Abstract

fetched live from OpenAlex

For many years, data about heart failure (HF) was only limited to Western countries but in the last few years, well designed heart failure registries have been conducted in many developing countries. The purpose of this review is to summarize the current status regarding the epidemiology and management of heart failure in Middle East Arab countries (MEACs) by analysis of the results of the latest HF registries performed in these countries and to anticipate future perspectives, quality initiatives and areas of research and development. Data has shown that the average age of affected individuals is at least 10 years younger than their Western counterparts. Heart failure with preserved ejection fraction was generally under-represented in these registries to less than 30% of the whole population of heart failure. Coronary artery disease (CAD) constitutes about 55% of causes of heart failure in this region in comparison to about 70% in Western countries. An area that needs development is the investment in establishing specialized heart failure programs to cut the circle of non-compliance and repeated HF admissions to the hospitals. Advances in heart transplantation and mechanical circulatory support will continue to slow down and we are not expecting major changes in the near future but on the other hand, implantation of electronic devices like ICD and CRT is expected to increase significantly in the coming years in these 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 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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.191
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.310
Teacher spread0.274 · 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.

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

Citations55
Published2020
Admission routes1
Has abstractyes

Explore more

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