Hip arthroscopy utilization and reoperation rates in Ontario: a population-based analysis comparing different age cohorts
Bibliographic record
Abstract
Background: Older age (> 40 yr) and osteoarthritis are negative prognostic variables for hip arthroscopy, but their impact has not been quantified from a population standpoint. The purpose of this study was to perform a population-based analysis of hip arthroscopy utilization and associated 2- and 5-year reoperation rates and complications in different age cohorts. Methods: Administrative databases from Ontario, Canada, were retrospectively reviewed to identify patients aged 18–60 years who underwent hip arthroscopy between 2006 and 2016. Patients were stratified into 2 cohorts: 18–39 and 40–60 years of age. Patients were followed for 2 and 5 years to capture the occurrence of subsequent surgery (repeat arthroscopy or total hip arthroplasty) and postoperative complications. Results: A total of 1906 patients underwent hip arthroscopy, 818 (42.9%) of whom were aged 40–60 years. In the entire cohort, revision surgery occurred in 6.5% and 15.1% of cases at 2 and 5 years, respectively. Revision surgery rates were significantly higher among patients aged 40–60 years at 2 (10.8% v. 3.2%, p < 0.001) and 5 years (22.7% v. 8.2%, p < 0.001) than among those aged 18–39 years. Revision rates were higher among patients aged 50–60 years than among those aged 40–49 years at 2 years (14.3% v. 9.1%, p = 0.027). Complication rates did not differ between cohorts. Regression analysis revealed higher 2- and 5-year odds of secondary surgery in patients aged 40–49 years (odds ratio [OR] 2.68, 95% confidence interval [CI] 1.70–4.22; OR 2.82, 95% CI 1.87–4.25; p < 0.001), patients aged 50–60 years (OR 4.39, 95% CI 2.67–7.22; OR 3.44, 95% CI 2.11–5.62; p < 0.001) and those with osteoarthritis (OR 2.41, 95% CI 1.39–4.20; p = 0.002; OR 1.76, 95% CI 1.00–3.09; p = 0.049). Conclusion: Revision surgery rates following hip arthroscopy are significantly higher among older patients and those with concomitant osteoarthritis. Although the data have limitations, they provide useful information to guide surgical decision-making.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".