Cost-effectiveness of shoulder arthroplasty for osteoarthritis and rotator cuff tear arthropathy. An economic analysis using real-world data
Bibliographic record
Abstract
INTRODUCTION: This study aimed to assess cost-effectiveness of shoulder arthroplasty for osteoarthritis (OA) and rotator cuff tear arthropathy (CTA) from the perspective of a publicly funded health care system using patient data, health utilities and costs from a real-world situation. HYPOTHESIS: Our hypothesis was that arthroplasty for OA is more cost-effective than for CTA. MATERIAL AND METHODS: We gathered a cohort of patients with 153 anatomic total shoulder arthroplasty (TSA) for OA and 107 reverse shoulder arthroplasty (RSA) for CTA between years 2016-2020 at a university hospital. Short-term (mean 2.8years) shoulder function, health utilities and costs were obtained from prospectively collected data, and a Markov cohort simulation was carried out to assess lifetime cost-utility. The primary outcome measures were change in 15D score to calculate gain in quality-adjusted life years (QALYs) and change in Western Ontario osteoarthritis score of the shoulder (WOOS). RESULTS: Both TSA and RSA restored shoulder function well, WOOS improvement was 59.7 (95% CI: 56.2-63.2) and 55.8 (95% CI: 50.4-61.2), respectively. The cost/QALY gained was 20,846.82 € for TSA and 38,711.90 € for RSA. The cost-utility was not remarkable sensitive to costs, discounting of future costs or estimated revision rates. However, the cost-effectiveness was very sensitive to change in 15D health utility scores and thus QALY gain, especially for RSA patients. DISCUSSION: Shoulder arthroplasty restores shoulder function well in both OA and CTA. In health economic terms, RSA is less cost-effective than TSA in an everyday setting, mainly due to inferior improvement of health-related quality-of-life and reduced life expectancy of CTA patients. LEVEL OF EVIDENCE: III; case series.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".