Disparities in the reporting of distribution of health care
Bibliographic record
Abstract
We read with great interest the article by Wong et al.1 entitled “Ethnic Disparities and Liver Transplantation Rates in Hepatocellular Carcinoma Patients in the Recent Era: Results From the Surveillance, Epidemiology, and End Results Registry.” The authors concluded that ethnic minorities (Hispanics, blacks, and Asians) with hepatocellular carcinoma were significantly less likely to undergo liver transplantation than non-Hispanic whites. They focused on the 4 main ethnic groups and decided to exclude combination ethnicities such as black/Hispanic, Asian/Hispanic, and American Indian/Alaskan Native because the small numbers precluded a precise statistical evaluation. We laud any attempt to reveal disparities in the distribution of health care both in the present era and in the past.2-5 However, we maintain that Asians, blacks, 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 exhibits different medical and social issues in comparison with one another and whites. Asians, who represent the fastest growing ethnic group in the United States, are frequently evaluated as a single homogeneous entity. However, they include Chinese, Filipinos, Indians, Vietnamese, Koreans, Japanese, Pakistanis, Sri Lankans, Nepalese, Cambodians, Thai, Bangladeshis, and Burmese (Myanmarese). Once again, these subgroups frequently display different medical and social issues in comparison with one another and whites. Finally, blacks can be African American, African, Caribbean, European, or Hispanic (as stated in the article) as well as Canadian or Asian. As stated, there are different cardiovascular risk profiles and socioeconomic issues between and among these groups. We understand that it would be extremely tedious to break down each ethnic group into multiple subgroups. However, a careful analysis may have significant ramifications in terms of expectations, center evaluations, and reimbursement. Toward that end, we are currently evaluating the Scientific Registry of Transplant Recipients database for the last 10 years, and we will attempt to analyze ethnic data with these subcategorizations in mind. We hope that this analysis will shed further light on the health care disparities so eloquently demonstrated in this article by Wong et al.1 Afshin Parsikia, M.D., M.P.H. Jorge Ortiz, M.D. Department of Transplant Surgery Einstein Healthcare Network Einstein Medical Center Philadelphia, PA
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".