The effect of Medicaid expansion on female gynecologic cancer-related inpatient admissions
Bibliographic record
Abstract
BACKGROUND: Mortality rate of female gynecologic cancer is higher among individuals without affordable health insurance. OBJECTIVES: We determined the impact of Medicaid expansion on the number of female gynecologic-related cancer inpatient admissions in Virginia (VA) relative to North Carolina (NC), the latter of which did not expand Medicaid. DESIGN: This quasi-experimental study was restricted to women between 18 and 64 years old admitted to general, acute, and short-term hospitals with gynecologic cancer. METHODS: We used Poisson fixed-effect event study regression to examine differences in the predicted number of female gynecologic-related cancer admissions in the quarters before and after Medicaid expansion (implemented in January 2019) in VA, compared to the same period in NC. RESULTS: Even though not significant, the predicted number of female gynecologic cancer-related inpatient admissions in VA increased by 4.8%, 4.9%, and 5.5% in the second, third, and fourth quarter of 2019, respectively, compared to the first quarter of 2019. CONCLUSION: Medicaid expansion in VA increased access to health services for Medicaid members, possibly due to initial pent-up demand among uninsured individuals.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".