EP1.6 Cost-Utility Analysis of Osteochondroplasty Compared to Lavage for Treatment of Young Adults with Femoroacetabular Impingement in Canada
Bibliographic record
Abstract
Abstract Objective: To conduct a cost-utility analysis of osteochondroplasty compared to lavage for femoroacetabular impingement (FAI) from a Canadian public payer perspective. Methods: A Markov model was constructed to compare the lifetime quality-adjusted life years (QALYs) and costs of the two treatment strategies. The target population was surgical FAI patients aged 36 years. The primary data source was patient-level data from the Femoroacetabular Impingement Randomised Controlled Trial (FIRST), which evaluated the efficacy of the surgical correction of femoroacetabular impingement (FAI) via arthroscopic osteochondroplasty compared with arthroscopic lavage with or without labral repair. Long-term data were extrapolated using a generalized gamma model. The primary outcome was the incremental cost-effectiveness ratio (ICER), calculated by dividing the difference in costs by the difference in QALYs between osteochondroplasty and lavage. Probabilistic sensitivity analyses and one-way sensitivity analyses were used to characterize uncertainty of model parameters and assumptions. Results: Over a lifetime horizon, osteochondroplasty had a greater expected benefit (0.63 QALYs gained per patient) and lower costs ($955.89 saved per patient), as compared with lavage. Probabilistic sensitivity analyses demonstrated that the probability of osteochondroplasty being cost-effective was 90.5% at a commonly used willingness-to-pay threshold of $50,000/QALY. Across all one-way sensitivity analyses, osteochondroplasty remained a cost-effective option. Conclusion: Over a lifetime time horizon, osteochondroplasty is a cost-effective treatment strategy for young adults with FAI. Future research involving real-word data is needed to further validate these findings.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".