COST-ANALYSIS OF TOTAL HIP ARTHROPLASTY: DOES SURGICAL APPROACH MATTER?
Bibliographic record
Abstract
Introduction Total hip arthroplasty (THA) is a commonly performed surgical procedure for the treatment of hip arthritis. Approximately 50,000 THAs are performed annually in Canada. The costs incurred to the healthcare system are tremendous, amounting to anywhere between 4.3 and 7.3 billion dollars each year. Despite the substantial financial burden of THA to the Canadian healthcare system, few studies have provided accurate cost estimations of this procedure. Purpose To determine the impact of surgical approach on costs of THA from a hospital perspective, and provide an updated cost estimation of THA within a publically funded healthcare system. Methods We recruited patients undergoing a THA through an anterior, posterior, or lateral approach for study participation. A single surgeon was designated to perform every case using the surgical approach of their expertise. Each patient received standardized implants: a collared, hydroxyapatite-coated, cementless femoral stem (Corail TM stem, DePuy Orthopaedics Inc., Warsaw, IN), a cementless acetabular cup (Pinnacle Sector II TM acetabular cup, DePuy Orthopaedics Inc., Warsaw, IN), a highly cross-linked polyethylene liner (AltrX TM polyethylene liner, DePuy Orthopaedics Inc., Warsaw, IN), and a cobalt chrome femoral head (Articul/eze TMcobalt chrome, DePuy Orthopaedics Inc., Warsaw, IN). We prospectively recorded costs of operating room time, length of stay in hospital, and medical and surgical interventions using a micro-costing method. Group comparisons were performed using Pearson9s Chi-square and one-way Analysis of Variance (ANOVA), with post-hoc testing when necessary. All costs were reported in 2013 Canadian dollars. Results One-hundred and eighteen patients (40 anterior, 38 posterior, and 40 lateral) completed the study. All three groups were similar with regards to age (p=0.79), sex (p=0.97), and body mass index (p=0.54). Mean operating room time was significantly shorter for the lateral (49.0 minutes, 95%CI 46.5 – 51.5) versus anterior (69.3 minutes, 95%CI 66.0 – 72.6, p Discussion / Conclusion Total costs for THA were significantly less when performed using an anterior approach. A reduction in hospital length of stay contributed significantly to an overall reduction in costs from a hospital perspective. Future analyses will determine the cost-effectiveness of the anterior approach from both a hospital and societal perspective.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".