Satisfaction, functional outcomes and predictors in hip arthroscopy: a cohort study
Bibliographic record
Abstract
Introduction: Hip arthroscopy is not always successful, leading to high rates of total hip arthroplasty (THA) after arthroscopy. The aim of this study was to identify risk factors for THA, revision arthroscopy and low patient satisfaction and to compare outcomes of the different procedures of primary hip arthroscopy. Methods: A total of 91 primary hip arthroscopy procedures in 90 patients (66% female) were analysed. Data were gathered from patient files and a questionnaire was sent to patients including the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), modified Harris Hip Score (mHHS), the EuroQol 5-dimension and questions about return to sports, satisfaction and pain before and after surgery. Using regression analyses, predictive factors for the outcomes were identified. Results: After a mean of 1.6 years, 4 patients (4%) underwent revision arthroscopy and 10 (11%) a THA. Of the responders (62%), 66% of the patients was satisfied to very satisfied about the surgery. Mean mHHS score was 75.3 (SE 1.9) and the mean WOMAC score was 81.0 (SE 2.8). Return to sports rate was 58%. A higher age was a significant predictor for lower satisfaction ( p = 0.008) and a longer duration of symptoms was a significant predictor for worse mHHS outcome scores ( p = 0.005). Conclusion: A higher age is a predictor for a lower satisfaction and a longer duration of symptoms before surgery has a negative influence on functional outcome. No risk factors for THA or revision arthroscopy were found and there were no significant differences in outcome measurements between the performed surgeries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".