Insurance Payer Type and Patient Income Are Associated with Outcomes after Total Shoulder Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To assess the independent association of insurance and patient income with total shoulder arthroplasty (TSA) outcomes. METHODS: We used the 1998-2014 US National Inpatient Sample. We used multivariable-adjusted logistic regression to examine whether insurance type and the patient's median household income (based on postal code) were independently associated with healthcare use (discharge destination, hospital stay duration, total hospital charges) and in-hospital complications post-TSA based on the diagnostic codes (fracture, infection, transfusion, or revision surgery). We calculated the OR and 95% CI. RESULTS: Among the 349,046 projected TSA hospitalizations, the mean age was 68.6 years, 54% were female, and 73% white. Compared to private insurance, Medicaid and Medicare (government insurance) users were associated with significantly higher adjusted OR (95% CI) of (1) discharge to a rehabilitation facility, 2.16 (1.72-2.70) and 2.27 (2.04-2.52); (2) hospital stay > 2 days, 1.65 (1.45-1.87) and 1.60 (1.52-1.69); and (3) transfusion, 1.35 (1.05-1.75) and 1.39 (1.24-1.56), respectively. Medicaid was associated with a higher risk of fracture [1.74 (1.07-2.84)] and Medicare user with a higher risk of infection [2.63 (1.24-5.57)]; neither were associated with revision. Compared to the highest income quartile, the lowest income quartile was significantly associated with (OR, 95% CI): (1) discharge to a rehabilitation facility (0.89, 0.83-0.96); (2) hospital stay > 2 days (0.84, 0.80-0.89); (3) hospital charges above the median (1.19, 1.14-1.25); (4) transfusion (0.73, 0.66-0.81); and (5) revision (0.49, 0.30-0.80), but not infection or fracture. CONCLUSION: This information can help to risk-stratify patients post-TSA. Future assessments of modifiable mediators of these complications are needed.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".