Reverse Total Shoulder Arthroplasty Versus Hemiarthroplasty for the Treatment of Proximal Humerus Fractures: A Model-Based Cost-Effectiveness Analysis
Bibliographic record
Abstract
INTRODUCTION: Compared with hemiarthroplasty (HA), reverse total shoulder arthroplasty (RTSA) may provide greater cost and health-related benefits for patients with complex three- and four-part proximal humeral fractures. This study set out to compare RTSA versus HA for the incremental cost per incremental improvement in quality adjusted life years (QALYs) for a hypothetical cohort of patients with proximal humerus fractures. METHODS: Parameters and characteristics for a hypothetical cohort of elderly patients with proximal humerus fractures were collected through the literature. A cohort-level Markov decision model was constructed. Incremental cost-effectiveness ratios representing the difference in cost divided by the difference in QALYs were calculated, and scenario, one-way, and probabilistic analysis were conducted. RESULTS: RTSA was associated with lower cost and greater effectiveness compared with HA. The predicted cost difference corresponded to a saving of $99,626 per 100 individuals treated, and the predicted difference in QALY was 16.8 per 100 individuals treated. Results were sensitive to the discount rate, the health-related quality of life assigned to health states, and the cost of the surgical procedures. In probabilistic analysis, 77.1% of iterations were cost-effective at a threshold willingness-to-pay for a QALY of $100,000 US dollars. DISCUSSION: Findings suggest that RTSA may be a cost-effective alternative to HA for treating elderly patients requiring surgery for proximal humerus fractures. DATA AVAILABILITY: The model and corresponding code are available on request to the corresponding author. LEVEL OF EVIDENCE USING THE JOURNAL OF THE AMERICAN ACADEMY OF ORTHOPEDIC SURGEONS GUIDANCE: Level III.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".