South Asian Ethnicity as a Risk Factor for Major Adverse Cardiovascular Events after Renal Transplantation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: South Asians (SAs) comprise 25% of all Canadian visible minorities. SAs constitute a group at high risk for cardiovascular disease in the general population, but the risk in SA kidney transplant recipients has never been studied. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In a cohort study of 864 kidney recipients transplanted from 1998 to 2007 and followed to June 2009, we identified risk factors including ethnicity associated with major cardiac events (MACEs, a composite of nonfatal myocardial infarction, coronary intervention, and cardiac death) within and beyond 3 months after transplant. Kaplan-Meier methodology and multivariate Cox regression analysis were used to determine risk factors for MACEs. RESULTS: There was no difference among SAs (n = 139), whites (n = 550), blacks (n = 65), or East Asians (n = 110) in baseline risk, including pre-existing cardiac disease. Post-transplant MACE rate in SAs was 4.4/100 patient-years compared with 1.31, 1.16, and 1.61/100 patient-years in whites, blacks, and East Asians, respectively (P < 0.0001 versus each). SA ethnicity independently predicted MACEs along with age, male gender, diabetes, systolic BP, and prior cardiac disease. SAs also experienced more MACEs within 3 months after transplant compared with whites (P < 0.0001), blacks (P = 0.04), and East Asians (P = 0.006). However, graft and patient survival was similar to other groups. CONCLUSIONS: SA ethnicity is an independent risk factor for post-transplant cardiac events. Further study of this high-risk group is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".