Regional variations in heart failure: a global perspective
Bibliographic record
Abstract
Heart failure (HF) is a global public health concern that affects millions of people worldwide. While there have been significant therapeutic advancements in HF over the last few decades, there remain major disparities in risk factors, treatment patterns and outcomes across race, ethnicity, socioeconomic status, country and region. Recent research has provided insight into many of these disparities, but there remain large gaps in our understanding of worldwide variations in HF care. Although the majority of the global population resides across Asia, Africa and South America, these regions remain poorly represented in epidemiological studies and HF trials. Recent efforts and registries have provided insight into the clinical profiles and outcomes across HF patterns globally. The prevalence of HF and associated risk factors has been reported and varies by country and region ranges, with minimal data on regional variations in treatment patterns and long-term outcomes. It is critical to improve our understanding of the different factors that contribute to global disparities in HF care so we can build interventions that improve our general cardiovascular health and mitigate the social and economic cost of HF. In this narrative review, we hope to provide an overview of the global and regional variations in HF care and outcomes.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.004 | 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".