Assessing health care disparities in US organ procurement organizations
Bibliographic record
Abstract
There is extensive system-wide evidence of disparities in access to organ transplantation in the US based on race, ethnicity, and socioeconomic status. However, little information is available regarding care disparities among US organ procurement organizations (OPOs). Commissioned by the US Centers for Medicare and Medicaid Services (CMS), we studied racial/ethnic disparities in organ donation and transplantation across and within OPOs. Based on the 2020 CMS final rule, we calculated OPO donation and organ transplantation rates with 95% confidence intervals for racial (Black, White, and Asian American and Pacific Islander, AAPI) and ethnic (Hispanic and non-Hispanic) groups. OPOs were ranked with national rates as references and classified according to the CMS 3-tier system. Of the 58 OPOs, 8 and 4 had donation rates lower for Black and AAPI donors than for White donors; 21 and 18 had organ transplantation rates lower for Black and AAPI donors than for White donors; 1 and 1 had a donation rate or organ transplantation rate lower for Hispanic donors than for non-Hispanic donors. Significant racial/ethnic disparities in organ donation and transplantation exist among many OPOs, whereas the overall OPO performance is dominated by White and non-Hispanic donors. These disparities may be influenced by variations and structural barriers in resource access, donor identification, transplantation referral, and waitlisting processes-some of which lie partially outside the direct control of OPOs and disproportionately affect disadvantaged populations. Results support equitable organ donation and allocation through enhanced awareness of health care disparities, increased accountability of OPOs, and informed policies and interventions.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".