Heart Failure In The Middle East Arab Countries: Current And Future Perspectives
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".