The Impact of Policy Reform on Utilization of Popular Reconstructive Procedures
Bibliographic record
Abstract
OBJECTIVES: Medicaid beneficiaries systematically face challenges in accessing healthcare, especially with regard to specialty services like reconstructive surgery. This study evaluated the impact of 2 healthcare reform policies, Medicaid expansion and global hospital budgeting, on utilization of reconstructive surgery by Medicaid patients. METHODS: Utilization of reconstructive surgery by Medicaid patients in New Jersey (Medicaid expansion/no global budget), Maryland (Medicaid expansion/with global budgets), and Florida (no Medicaid expansion/no global budget) between 2012 and 2016 was compared using quasi-experimental, interrupted time-series modeling. Subgroup analyses by procedure type and urgency were also undertaken. RESULTS: During the study period, the likelihood of Medicaid patients using reconstructive surgery significantly increased in expansion states (Maryland: 0.3% [95% confidence interval = 0.17% to 0.42%] increase per quarter, P < 0.001; New Jersey: 0.4% [0.31% to 0.52%] increase per quarter, P = 0.004) when compared with Florida (nonexpansion state). Global budgeting did not significantly impact overall utilization of reconstructive procedures by Medicaid beneficiaries. Upon subgroup analyses, there was a greater increase in utilization of elective procedures than emergent procedures by Medicaid beneficiaries after Medicaid expansion (elective: 0.9% [0.8% to 1.3%] increase per quarter, P = 0.04; emergent/urgent: 0.2% [0.1% to 0.4%] increase per quarter, P = 0.02). In addition, Medicaid expansion had the greatest absolute effect on breast reconstruction (1.0% [95% confidence interval = 0.7% to 1.3%] increase per quarter) compared with other procedure types. CONCLUSIONS: Medicaid expansion increased access to reconstructive surgery for Medicaid beneficiaries, especially for elective procedures. Encouragingly, although cost-constrictive, global hospital budgeting did not limit longitudinal utilization of reconstructive surgery by Medicaid patients, who are traditionally at higher risk for complications/readmissions.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".