Cost-Effectiveness Analysis of Motion-Preserving Operations for Wrist Arthritis
Bibliographic record
Abstract
BACKGROUND: The authors conducted a cost-effectiveness analysis to answer the question: Which motion-preserving surgical strategy, (1) four-corner fusion, (2) proximal row carpectomy, or (3) total wrist arthroplasty, used for the treatment of wrist osteoarthritis, is the most cost-effective? METHODS: A simulation model was created to model a hypothetical cohort of wrist osteoarthritis patients (mean age, 45 years) presenting with painful wrist and having failed conservative management. Three initial surgical treatment strategies-(1) four-corner fusion, (2) proximal row carpectomy, or (3) total wrist arthroplasty-were compared from a hospital perspective. Outcomes included clinical outcomes and cost-effectiveness outcomes (quality-adjusted life-years and cost) over a lifetime. RESULTS: The highest complication rates were seen in the four-corner fusion cohort: 27.1 percent compared to 20.9 percent for total wrist arthroplasty and 17.4 percent for proximal row carpectomy. Secondary surgery was common for all procedures: 87 percent for four-corner fusion, 57 percent for proximal row carpectomy, and 46 percent for total wrist arthroplasty. Proximal row carpectomy generated the highest quality-adjusted life-years (30.5) over the lifetime time horizon, compared to 30.3 quality-adjusted life-years for total wrist arthroplasty and 30.2 quality-adjusted life-years for four-corner fusion. Proximal row carpectomy was the least costly; the mean expected lifetime cost for patients starting with proximal row carpectomy was $6003, compared to $11,033 for total wrist arthroplasty and $13,632 for four-corner fusion. CONCLUSIONS: The authors' analysis suggests that proximal row carpectomy was the most cost-effective strategy, regardless of patient and parameter level uncertainties. These are important findings for policy makers and clinicians working within a universal health care system.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".