Despite its current widespread use, evidence to support the indications for hip arthroscopy lags behind: a review of current literature
Bibliographic record
Abstract
Importance Hip arthroscopy is commonly used for diagnostic purposes and for treatment of a variety of hip pathologies. Despite its current widespread use, evidence to support the indications for its application may lag behind. Objective Review the body of evidence for commonly accepted indications (ie, femoroacetabular impingement (FAI), labral tear, septic hip, loose bodies, extra-articular lesions, mild/moderate osteoarthritis, extra-articular impingement, abductor tendon tears and labral reconstruction) for hip arthroscopy based on recent systematic reviews. Evidence review A literature review was performed (in August 2015) using the PubMed database. The search results were filtered to include only the most recent (past 4 years 2012–2015) systematic reviews. Only articles that were selected for inclusion by these systematic reviews were included in this review of indications. Articles were then combined by indication and sorted by level of evidence and assigned a Grade of Recommendation. Findings Fair evidence exists to support the arthroscopic repair of acetabular labral tears and for the treatment of symptomatic FAI. Poor-quality evidence exists to support the use of hip arthroscopy in the treatment of extra-articular impingement, septic arthritis, mild/moderate osteoarthritis, abductor tendon tears and labral reconstruction. No recommendation could be made on the use of hip arthroscopy for ischiofemoral impingement, greater trochanter pelvic impingement and treatment for asymptomatic FAI. Conclusions and relevance There has been a large increase in published studies on hip arthroscopy; however, the studies published hitherto are generally of lesser quality and as such do not provide a higher Grade of Recommendation for most hip arthroscopy procedures.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".