A Geographic Population-level Analysis of Access to Total Shoulder Arthroplasty in the State of Texas
Bibliographic record
Abstract
INTRODUCTION: Managing costs and improving access to care are two important goals of healthcare policy. The purposes of this study were to (1) evaluate the changes in distribution of total shoulder arthroplasty (TSA) cases in the state of Texas from 2010 to 2015 and (2) to evaluate patient access to TSA surgery centers as measured by driving miles. METHODS: Inpatient (IP) and outpatient (OP) records were obtained from 2010 to 2015 from the Texas Department of State Health Services. All primary elective anatomic or reverse TSAs for patients with Texas-based home residence zip codes were included. Driving miles between patient zip codes and their chosen TSA surgery centers were estimated, and the results were compared between IP (high-volume [HV-IP] or low-volume [LV-IP]) and OP centers. Paired student t-tests, multivariate regressions, and mixed-model analysis of variance (ANOVA) were performed for volume comparisons, interactions between TSA centers types, and yearly trend data, respectively. RESULTS: Between 2010 and 2015, a total of 21,092 TSA procedures were performed across 321 surgery centers in the state of Texas (19,629 IP [93.1%] and 1,463 OP [6.9%]). During this time, the cumulative volume of IP TSA per 100,000 Texas residents increased by 109.1%, whereas the cumulative volume of OP TSA increased by 143.7%. Approximately 85.5% of included patients resided within 50 miles of any TSA surgery center; however, only 47.0% of the total Texas population resided within 50 miles of any TSA surgery center. This relationship remained true at every time point irrespective of their volume designations (OP, IP, HV-IP, and LV-IP). CONCLUSION: Despite the overall increase in TSA volume over time, the majority all TSA utilization in the state of Texas occurred in patients who resided within 50 miles of a TSA center. Increasing volume seems to reflect concentration of care into HV-IP and OP centers. Strategies to improve access to TSA care for underserved areas should be considered. LEVEL OF EVIDENCE: Level II.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".