Impact of Medicaid expansion on kidney transplantation in the State Oklahoma
Bibliographic record
Abstract
BACKGROUND There is no data evaluating the impact of Medicaid expansion on kidney transplants (KT) in Oklahoma. AIM To investigate the impact of Medicaid expansion on KT patients in Oklahoma. METHODS The UNOS database was utilized to evaluate data pertaining to adult KT recipients in Oklahoma in the pre-and post-Medicaid eras. Bivariate analysis, Kaplan Meier analysis was used to estimate, and cox proportional models were utilized. RESULTS There were 2758 pre- and 141 recipients in the post-Medicaid expansion era. Post-expansion patients were more often non-United States citizens (2.3% vs 5.7%), American Indian, Alaskan, or Pacific Islander (7.8% vs 9.2%), Hispanic (7.4% vs 12.8%), or Asian (2.5% vs 8.5%) (P < 0.0001). Waitlist time was shorter in the post-expansion era (410 vs 253 d) (P = 0.0011). Living donor rates, pre-emptive transplants, re-do transplants, delayed graft function rates, kidney donor profile index values, panel reactive antibodies levels, and insurance types were similar. Patients with public insurance were more frail. Despite increased early (< 6 months) rejection rates, 1-year patient and graft survival were similar. In Cox proportional hazards model, male sex, American Indian, Alaskan or Pacific Islander race, public insurance, and frailty category were independent risk factors for death at 1 year. Medicaid expansion was not associated with graft failure or patient survival (adjusted hazard ratio: 1.07; 95%CI: 0.26-4.41). CONCLUSION Medicaid expansion in Oklahoma is associated with increased KT access for non-White/non-Black and non-United States citizen patients with shorter wait times. 1-year graft and patient survival rates were similar before and after expansion. Medicaid expansion itself was not independently associated with graft or patient survival outcomes. Ongoing research is necessary to determine the long-term effects of Medicaid expansion.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".