Disparities in the reporting of distribution of health care
Bibliographic record
Abstract
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.
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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 | 0.032 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| 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".