Clinical genetics laboratories use divergent demographic frameworks across countries: comparing data structures for ‘race’, ‘ethnicity’, and ‘ancestry’ on test requisition forms
Bibliographic record
Abstract
ABSTRACT Purpose The goal of this study is to investigate how population groups are represented on requisition forms for clinical genetic testing in different laboratories. Methods Clinical laboratory test requisition forms (RFs) were obtained from 70 laboratories in the US, Canada, Europe, and Australia. Details about the laboratories and how RFs represent patient demographics were extracted and analyzed for trends between forms in the U.S. (N=213) and other countries (N=203). Results Clinical genetics laboratories included in the analysis vary widely regarding the format of demographic data collected on test requisition forms. US-based laboratory RFs are more likely than those from other countries to include ‘race’ or ‘ethnicity’. These are most often represented as categorical data, with multiple-choice options. RFs from laboratories in other countries do not include ‘race’, and those that include ‘ethnicity’ most often provide a blank space for open-ended responses. Conclusions These results are consistent with existing research on heterogeneity in the nomenclature and number of categories used to describe patient populations across clinical genetics laboratories in the US. It also suggests systemic differences in the way measures of diversity are conceptualized in the US compared to other countries.
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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 | 0.035 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".