Cardiometabolic-Renal Disease in South Asians: Consensus Recommendations from the Cardio Renal Society of America
Bibliographic record
Abstract
BACKGROUND: Rates of cardiometabolic-renal disease are extremely high among South Asians (India, Pakistan, Bangladesh, Sri Lanka, Bhutan, the Maldives, and Nepal) residing in their home countries and worldwide. The Cardio Renal Society of America, National Kidney Foundation of Arizona, and Twinepidemic Inc. convened a task force to examine evidence and reach consensus regarding cardiometabolic-renal disease prevention in South Asians. The task force distilled the findings from 5 years of face-to-face and virtual meetings addressing questions derived from expert reviews of published data using the Delphi technique to create these consensus statements. SUMMARY: Several high-quality observational studies document the high and increasing incidence and prevalence of cardiometabolic-renal disease among South Asians, starting well before adulthood, owing to genetic, cultural, and environmental factors. Despite the need for additional prospective studies, especially randomized trials, of educational, screening, and other prevention efforts, sufficient information is already available to expand and intensify ongoing efforts in professional and lay education to help control this epidemic. The task force proposes to provide this expansion over the next 10 years through scientific and lay publications and other educational programs to promote more effective action among the public, health care professionals, payers, and regulators in screening for and treating cardiometabolic-renal risk factors and preventing disease in South Asians, starting at an early age. Key Messages: These consensus statements describe risk factors and prognoses characteristic of South Asians regarding cardiometabolic-renal diseases, to aid physician decision-making, health care system delivery, and research initiatives to improve the quality of care for South Asians worldwide.
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 | 0.094 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".