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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".