ReviewHLA2023
Population designations in biomedical research: Limitations and perspectives.
Review in HLA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 18 citations in OpenAlex.
- From pancreas and islet resources to diabetes insights.Diabetologia · 2026Review
- Efficacy and safety of immunotherapy in elderly patients with advanced non-small cell lung cancer: a real-world study stratified by age.BMC geriatrics · 2026Article
- Experience using conventional compared to ancestry-based population descriptors in clinical genomics laboratories.American journal of human genetics · 2025Review
- Quantifying uncertainty of molecular mismatch introduced by mislabeled ancestry using haplotype-based HLA genotype imputation.Frontiers in genetics · 2024Article
- Review
- Ancestry and self-reported race in Brazilian breast cancer women.Revista da Associacao Medica Brasileira (1992) · 2023Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In biomedical research, population differences are of central interest. Variations in the frequency and severity of diseases and in treatment effects among human subpopulation groups are common in many medical conditions. Unfortunately, the practices in terms of subpopulation labeling do not exhibit the level of rigor one would expect in biomedical research, especially when studying multifactorial diseases such as cancer or atherosclerosis. The reporting of population differences in clinical research is characterized by large disparities in practices, and fraught with methodological issues and inconsistencies. The actual designations such as "Black" or "Asian" refer to broad and heterogeneous groups, with a great discrepancy among countries. Moreover, the use of obsolete concepts such as "Caucasian" is unfortunate and imprecise. The use of adequate labeling to reflect the scientific hypothesis needs to be promoted. Furthermore, the use of "race/ethnicity" as a unique cause of human heterogeneity may distract from investigating other factors related to a medical condition, particularly if this label is employed as a proxy for cultural habits, diet, or environmental exposure. In addition, the wide range of opinions among researchers does not facilitate the attempts made for resolving this heterogeneity in labeling. "Race," "ethnicity," "ancestry," "geographical origin," and other similar concepts are saturated with meanings. Even if the feasibility of a global consensus on labeling seems difficult, geneticists, sociologists, anthropologists, and ethicists should help develop policies and practices for the biomedical field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.