Hip Labral Repair versus Reconstruction: Meta-analysis
Bibliographic record
Abstract
The purpose of this meta-analysis is to compare the postoperative outcomes and complications of labral repair with those of labral reconstruction.An electronic search strategy was conducted from 1986 until August 2023 using the following databases: PubMed, Cochrane, and Google Scholar (pages 1-20).The primary objectives included the postoperative clinical outcomes determined by the number of patients who reached minimal clinical important difference (MCID) on the visual analog scale (VAS), modified Harris hip score (mHHS), Hip Outcome Score-Sports Subscale (HOS-SS), Hip Outcome Score-Activities of Daily Life (HOS-ADL), and International Hip Outcome Tool-12 (iHOT-12).In addition, analysis of the rate of revision arthroscopy, the rate of conversion to total hip arthroplasty (THA), the postoperative VAS, mHHS, HOS-SS, HOS-ADL, iHOT-12, nonarthritic hip score (NAHS), patient satisfaction, lower extremity function scale (LEFS), and the SF-12 (12-item shortform) was also performed.Any differences arising between the investigators were resolved by discussion.Seventeen studies were relevant to the inclusion criteria and were included in this meta-analysis.A higher rate of patients who reached MCID in the mHHS (P=0.02) as well as a higher rate of revision arthroscopy was observed for labral repair (P=0.03).The remaining studied outcomes were comparable.Despite the greater predictability of success in the reconstruction group, conduct of additional studies will be required for evaluation of the benefits of such findings.In addition, labral reconstruction is more technically demanding than a 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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".