Total Shoulder Arthroplasty Is Cost-Effective Compared with Hemiarthroplasty
Bibliographic record
Abstract
BACKGROUND: Although outcome studies generally demonstrate the superiority of a total shoulder arthroplasty (TSA) over a hemiarthroplasty (HA), comparative cost-effectiveness has not been well studied. From a publicly funded health-care system's perspective, this study compared the costs and quality-adjusted life-years (QALYs) in patients who underwent TSA with those in patients who underwent HA. METHODS: We conducted a cost-utility analysis using a Markov model to simulate the costs and QALYs for patients undergoing either TSA or HA over a lifetime horizon to account for costs and medically important events over the patient lifetime. Subgroup analyses by age groups (≤50 or >50 years) were performed. A series of sensitivity analyses were performed to assess robustness of study findings. The results were presented in 2019 U.S. dollars. RESULTS: TSA was dominant as it was less costly ($115,785 compared with $118,501) and more effective (10.21 compared with 8.47 QALYs) than HA over a lifetime horizon. Changes to health utility values after TSA and HA had the largest impact on the cost-effectiveness findings. At a willingness-to-pay (WTP) threshold of $50,000 per QALY gained, HA was not found to be cost-effective. The probability that TSA was cost-effective was 100%. CONCLUSIONS: Based on a WTP of $50,000 per QALY gained, from the perspective of Canada's publicly funded health-care system, TSA was found to be cost-effective in all patients, including those ≤50 years of age, compared with HA. LEVEL OF EVIDENCE: Economic and Decision Analysis Level II. See Instructions for Authors for a complete description of levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.007 | 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".