Managing cardiovascular disease risk in South Asian kidney transplant recipients
Bibliographic record
Abstract
South Asians (SA) are at higher cardiovascular risk than other ethnic groups, and SA kidney transplant recipients (SA KTR) are no exception. SA KTR experience increased major adverse cardiovascular events both early and late post-transplantation. Cardiovascular risk management should therefore begin well before transplantation. SA candidates may require aggressive screening for pre-transplant cardiovascular disease (CVD) due to their ethnicity and comorbidities. Recording SA ethnicity during the pre-transplant evaluation may enable programs to better assess cardiovascular risk, thus allowing for earlier targeted peri- and post-transplant intervention to improve cardiovascular outcomes. Diabetes remains the most prominent post-transplant cardiovascular risk factor in SA KTR. Diabetes also clusters with other metabolic syndrome components including lower high-density lipoprotein cholesterol, higher triglycerides, hypertension, and central obesity in this population. Dyslipidemia, metabolic syndrome, and obesity are all significant CVD risk factors in SA KTR, and contribute to increased insulin resistance. Novel biomarkers such as adiponectin, apolipoprotein B, and lipoprotein (a) may be especially important to study in SA KTR. Focused interventions to improve health behaviors involving diet and exercise may especially benefit SA KTR. However, there are few interventional clinical trials specific to the SA population, and none are specific to SA KTR. In all cases, understanding the nuances of managing SA KTR as a distinct post-transplant group, while still screening for and managing each CVD risk factor individually in all patients may help improve the long-term success of all kidney transplant programs catering to multi-ethnic populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".