Conversion to Total Hip Arthroplasty After Hip Arthroscopy: A Cohort-Based Survivorship Study With a Minimum of 2-Year Follow-up
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to determine which patient, provider, and surgical factors influence progression to total hip arthroplasty (THA) after hip arthroscopy (HA) through a large cohort-based registry. METHODS: All patients ≥18 years who underwent unilateral HA in Ontario, Canada, between October 1, 2010, and December 31, 2016, were identified with a minimum of 2-year follow-up. The rate of THA after HA was reported using Kaplan-Meier survivorship analyses. A Cox proportional hazard model was used to assess which factors independently influenced survivorship. RESULTS: A total of 2,545 patients (53.2% female, mean age 37.4 ± 11.8 years) were identified. A total of 237 patients (9.3%) were identified to have undergone THA at a median time of 2 years after HA, with an additional 6.3% requiring a revision arthroplasty at a median time of 1.1 years. Patients who underwent isolated labral resection (hazard ratio [HR]: 2.55, 95% confidence interval [CI]: 1.51 to 4.60) or in combination with osteochondroplasty (OCP) [HR: 2.11, 95% CI: 1.22 to 3.88] were more likely to undergo THA versus patients who underwent isolated labral repair or in combination with an OCP, respectively. Older age increased the risk for THA (HR: 14.0, 95% CI: 5.76 to 39.1), and treatment by the highest-volume HA surgeons was found to be protective (HR: 0.55, 95% CI: 0.33 to 0.89). DISCUSSION: Using our methods, the rate of THA after HA was 9.3% at 2 years. The rate of revision arthroplasty was 6.3% at 1 year. Patients who underwent labral resection, isolated OCP, and/or were of increased age were at increased independent risk of conversion to THA. Those treated by the highest-volume HA surgeons were found to be at reduced risk of conversion to THA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".