Shoulder Arthroplasty for a Fracture Is Not the Same as Shoulder Arthroplasty for Osteoarthritis: Implications for a Bundled Payment Model
Bibliographic record
Abstract
INTRODUCTION: The Center for Medicare Services currently bundles all shoulder arthroplasties, total shoulder arthroplasty and reverse total shoulder arthroplasty, into one Diagnosis-Related Group on which bundled reimbursements are then further characterized. An arthroplasty performed for traumatic indications, such as fractures, may have a different postoperative course of care compared with the one being done for degenerative arthritis/osteoarthritis (OA), despite having the same Current Procedural Terminology (CPT) and Diagnosis-related Group code. METHODS: The 2012 to 2016 American College of Surgeons-National Surgical Quality Improvement Program databases were queried using CPT-23472 to retrieve records of patients undergoing total shoulder arthroplasty/reverse total shoulder arthroplasty for degenerative arthritis/OA or proximal humerus fracture. RESULTS: A total of 8,283 (92.5%) and 667 (7.5%) patients underwent a shoulder arthroplasty for OA and proximal humeral fracture, respectively. After adjustment, the fracture group was associated with a higher risk for a longer length of stay of >2 days (P < 0.001), 30-day surgical complications (P = 0.005), revision surgeries within 30 days (P = 0.008), 30-day medical complications (P < 0.001), pulmonary embolism (P = 0.013), postoperative transfusions (P < 0.001), non-home discharge (P < 0.001), and 30-day readmissions (P < 0.001). DISCUSSION: Shoulder arthroplasty is associated with higher resource utilization when this procedure is performed for a fracture. As we move toward the era of bundled payment models, an appropriate risk adjustment based on the indication of surgery should be promoted to maintain the quality of care for all patients.
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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 | 0.028 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".