Combined hip arthroscopy with periacetabular osteotomy for hip dysplasia: a systematic review
Bibliographic record
Abstract
Periacetabular osteotomy (PAO) is a surgical procedure that corrects acetabular dysplasia without necessarily addressing intra-articular pathology. Hip arthroscopy is being increasingly used to address soft tissue pathologies at the time of a PAO. This review aims to determine patient-reported outcome measure scores (PROMs) of combining hip arthroscopy and PAO. This systematic review followed the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines to identify English studies that reported upon patient populations that had PAO's performed with arthroscopy at the time of surgery for correcting developmental hip dysplasia. We identified 428 articles; 14 full-text articles met the inclusion criteria. Between 2011 and 2022, 1083 hips from the selected articles underwent a combined PAO and arthroscopic procedure, with a mean follow-up of 3.7 years. Of the studies that reported it, 63% of the evaluated population were found to have labral tears that required either labral repair (49%), labral debridement (12%) or combined procedure. Multiple PROMs were identified in the literature, with no standardized reporting system used between articles. All articles reported statistically improved patient-reported outcomes from a combined PAO and arthroscopy procedure. There was no difference in PROMs when comparing PAO performed with or without arthroscopy. One study suggested superior outcomes for active individuals who underwent PAO and arthroscopy. Patient-reported outcome scores improve significantly after PAO with or without arthroscopy, with no differences in adverse events, and only limited evidence that active individuals benefit from labral repair.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".