Double-Row Arthroscopic Rotator Cuff Repair Is More Cost-Effective Than Single-Row Repair
Bibliographic record
Abstract
BACKGROUND: The optimal technique for arthroscopic rotator cuff repair is controversial, and both single and double-row techniques are commonly used. In the current era of increasing costs, health-care delivery models are focusing on the value of care. In this study, we compared the cost-effectiveness of single-row and double-row reconstructions in patients undergoing arthroscopic rotator cuff repair. METHODS: A cost-utility analysis was performed from the perspective of a publicly funded health-care system. Health-care costs, probabilities, and utility values were derived from the published literature. Efficacy data were obtained from a previous randomized controlled trial comparing the effect of single-row (n = 48) or double-row (n = 42) reconstruction among 90 surgical patients. Unit cost data were obtained from a hospital database and the Ontario Schedule of Benefits and Fees. Results are presented as an incremental cost per quality-adjusted life year (QALY) gained. All costs are presented in 2015 Canadian dollars. A series of 1-way and probabilistic sensitivity analyses were performed. RESULTS: Double-row fixation was more costly ($2,134.41 compared with $1,654.76) but was more effective than the single-row method (4.073 compared with 4.055 QALYs). An incremental cost-effectiveness ratio (ICER) was estimated to be $26,666.75 per QALY gained for double-row relative to single-row fixation. A subgroup analysis demonstrated that patients with larger rotator cuff tears (≥3 cm) had a lower ICER, suggesting that double-row fixation may be more cost-effective for larger tears. CONCLUSIONS: Based on the willingness-to-pay threshold of $50,000 per QALY gained, double-row fixation was found to be more cost-effective than single-row. Furthermore, a double-row reconstruction was found to be more economically attractive for larger rotator cuff tears (≥3 cm). LEVEL OF EVIDENCE: Economic and Decision Analysis Level IV. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".