Bibliographic record
Abstract
PURPOSE OF REVIEW: Ethnic minority groups constitute increasing proportions of the population in western countries. Heart failure is increasingly prevalent worldwide and is associated with significant morbidity and mortality. The purpose of this review is to discuss the limited data on heart failure in the ethnic minority groups. RECENT FINDINGS: South Asians have more coronary risk factors that may increase the risk for premature coronary heart disease leading to development of heart failure at a younger age. In the Chinese, hypertension remains an important cause of heart failure and recent data suggest that heart failure with preserved systolic function is common. African-Americans have a higher prevalence of heart failure than whites, present with heart failure at younger ages, and heart failure in them is less likely to be due to coronary heart disease. Findings from a randomized controlled trial conducted specifically on African-Americans support the addition of the combination of isosorbide dinitrate and hydralazine to standard medical regimen for black patients with heart failure. Aboriginal people are more likely than nonaboriginal people to have less access to healthcare and to have a higher disease burden for atherosclerosis. Heart failure is more prevalent in aboriginal than in the nonaboriginal counterparts. SUMMARY: There are important differences across ethnic groups in the causes of heart failure and response to treatment. Given the likely increasing frequency of heart failure in these populations and an increasingly multiethnic world, additional studies on heart failure across different ethnic groups are warranted.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".