Racial referral bias in cardiac computed tomography: Differences, disparities or discrimination?
Bibliographic record
Abstract
BACKGROUND: Disparities exist in medicine and can affect patient care. We sought to understand influences of racial biases in diagnostic testing within a Cardiac CT (CCT) population. METHODS: Race of CCT patients, referring physicians and the population in the catchment area were captured between February 2006 and November 2021. The frequency of CCT referrals for each race was indexed to the catchment population. RESULTS: Of 21,241 CCT patients, 17,514 (82.5 %) patients were White. The Non-White population was comprised of 467(2.2 %) Indigenous, 656(3.1 %) Black, 932(4.4 %) Asian, 276(1.3 %) South Asian, 1100(5.2 %) Middle Eastern and 296(1.4 %) Latin American races. The catchment population was 907,675, with 619,514 individuals of whom 69.7 % identified as White. Compared to the catchment population, there was a disproportionately higher referral rate for Whites than Non-Whites. The referral index for Whites was higher than Non-Whites (1.2 versus 0.6, p < 0.001)). This pattern was consistent across all racial minorities and age categories. A total of 356 physicians (236(66.3 %) White, 4(1.2 %) Black, 39(12.0 %) Asian, 30(9.2 %) South Asian, 43(13.2 %), Middle Eastern and 4 (1.2 %) Latin American) made referrals to CCT. The racial difference in referral patterns was independent of physician race and was independent of their years in practice. CONCLUSIONS: Racial differences exist in CCT referrals. These differences are independent of prevalence of disease, physician race or years in practice. This study supports the need to better understand reasons for disparity and strategies to mitigate potential bias.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".