Use of Racial and Ethnic Categories in Medical Testing and Diagnosis: Primum Non Nocere
Bibliographic record
Abstract
BACKGROUND: Use of race and ethnicity is common in medical tests and procedures, even though these categories are defined by sociological, historical, and political processes, and vary considerably in their definitions over time and place. Because all societies organize themselves around these constructs in some way, they are undeniable facets of the human experience, with myriad health consequences. In the biomedical literature, they are also commonly interpreted as representing biological heterogeneity that is relevant for health and disease. CONTENT: We review the use of race and ethnicity in medical practice, especially in the USA, and provide 2 specific examples to represent a large number of similar instances. We then critique these uses along a number of different dimensions, including limitations in measurement, within- versus between-group variance, and implications for informativeness of risk markers for individuals, generalization from arbitrary or nonrepresentative samples, perpetuation of myths and stereotypes, instability in time and place, crowding out of more relevant risk markers, stigmatization, and the tainting of medicine with the history of oppression. We conclude with recommendations to improve practice that are technical, ethical, and pragmatic. SUMMARY: Medicine has evolved from a mystical healing art to a mature science of human health through a rigorous process of quantification, experimentation, and evaluation. Folkloric traditions, such as race- and ethnic-specific medicine will fade from use as we become increasingly critical of outdated and irrational clinical practices and replace these with personalized, evidenced-based tests, algorithms, and procedures that privilege patients' individual humanity over obsolete and misleading labels.
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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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".