Characterization of clinical study populations by race and ethnicity in biomedical literature.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The importance of race and ethnicity in biomedical research has long been a subject of debate, recently heightened by data revealed by the completion of the sequencing of the human genome and the mapping of human genetic variation. We aimed to determine whether and how the reporting of race has changed over the last three decades and how the practice may differ given study location, where the journal of publication is based, and decade of publication. DESIGN: We analyzed a sample of studies published in the Journal of the American Medical Association, The Lancet, and the Canadian Medical Association Journal from 1980 to 2009. MAIN OUTCOME MEASURES: The number of articles that reported race by journal and decade and the descriptors used. RESULTS: Of 1867 articles analyzed, 17.30% reported race. The reporting of race and number of populations reported increased over time for all three journals. In addition, the diversity of race/ethnicity descriptors increased, with increased use of race/ethnicity combinations and nationality of research subjects. CONCLUSION: Though it has increased over the past few decades, the reporting of race/ethnicity of study populations is relatively low, ambiguous and inconsistent, likely influenced by the uncertain relevance of these variables to the study's outcomes, study location, researcher views, and the policies of journals and funding agencies. Thus, due to the inconsistent and ambiguous practice of reporting race/ethnicity, comparison of study outcomes can result in misleading conclusions. Improvements in standardization of terms and new approaches to characterize research participants related to race/ethnicity are imperative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it