The Impact of Medicaid Expansion on Patients with Cancer in the United States: A Review
Bibliographic record
Abstract
Since 2014, American states have had the option to expand their Medicaid programs as part of the Affordable Care Act (ACA), which was signed into law by former President Barack H. Obama in 2010. Emerging research has found that Medicaid expansion has had a significant impact on patients with cancer, who often face significant financial barriers to receiving the care they need. In this review, we aim to provide a comprehensive examination of the research conducted thus far on the impact of Medicaid expansion on patients with cancer. We begin with a discussion of the history of Medicaid expansion and the key features of the ACA that facilitated it. We then review the literature, analyzing studies that have investigated the impact of Medicaid expansion on cancer patients in terms of access to care, quality of care, and health outcomes. Our findings suggest that Medicaid expansion has had a positive impact on patients with cancer in a number of ways. Patients in expansion states are more likely to receive timely cancer screening and diagnoses, and are more likely to receive appropriate cancer-directed treatment. Additionally, Medicaid expansion has been associated with improvements in cancer-related health outcomes, including improved survival rates. However, limitations and gaps in the current research on the impact of Medicaid expansion on patients with cancer exist, including a lack of long-term data on health outcomes. Additionally, further research is needed to better understand the mechanisms through which Medicaid expansion impacts cancer care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".