Regarding Outcomes in Ethnic Minority Renal Transplant Recipients Receiving Everolimus Versus Mycophenolate
Bibliographic record
Abstract
To the Editor We read with great interest the article “Outcomes in ethnic minority renal transplant recipients receiving everolimus versus mycophenolate: comparative risk assessment results from a pooled analysis” by Melancon et al (1). In it, they concluded that everolimus versus mycophenolate resulted in similar composite end point incidence events across ethnicities. They prudently analyzed African Americans separately from non-US blacks. Their results suggest that non-US black transplant recipients have similar outcomes as “white,” whereas “Asian” and “Hispanics” demonstrate improved outcomes. Melancon et al also corroborated previous reports indicating that African Americans have poorer clinical outcomes in all respects compared with all ethnicities. Whereas Melancon et al separately analyzed African Americans and non-US blacks, we maintain that “Asians” and “Hispanics” are overly simplistic generic groupings. For example, Hispanics in California and Texas are likely to be of Mexican heritage, whereas Hispanics in Florida are frequently Cuban. In New York, most Hispanics are Puerto Ricans. Each of these subgroups exhibit different medical and social issues compared with each other and whites. Asians, who represent the fastest growing ethnic group in the United States, are frequently evaluated as a single homogenous entity. However, it includes Chinese, Filipino, Indian, Vietnamese, Korean, Japanese, Pakistani, Sri Lankan, Nepalese, Cambodian, Thai, Bangladeshi, and Burmese (Myanmar). Early reports indicated that outcomes were superior among Asians (2–4). However, other reports show that South Asians (Indian, Pakistanis, Nepalese, and Sri Lankans) experience poorer outcomes after transplantation (5, 6). Interestingly, American studies of Asians tend to focus on East Asians (Chinese and Japanese). They usually conclude that these Asians have better outcomes. Canadian and British studies of Asians usually focus on Indo-Asians and usually report inferior outcomes. In our center in the North East region of the United States, we conducted a retrospective review of 91 Asian recipients over a decade. We found that Chinese Americans demonstrated better survival at 1 year than Whites and non-Chinese Asians. We understand that it would be extremely tedious to break down each ethnic group into multiple subgroups to study. However, careful analysis may yield significant ramifications in terms of expectations, center evaluation, and reimbursement. Afshin Parsikia 1 Farah Karipineni2 Jorge Ortiz1 1 Department of Transplant Einstein Medical Center Philadelphia, PA 2 Department of Surgery Einstein Medical Center Philadelphia, PA
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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 | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.008 |
| 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".