AB121. SOH24AB_042. Labral repair, reconstruction and augmentation improve postoperative outcomes in patients with irreparable or hypoplastic labra: a systematic review
Bibliographic record
Abstract
Background: Irreparable and hypoplastic labra of the hip pose a challenge for orthopaedic surgeons. This paper aims to review the outcomes of arthroscopic surgical options in treating these labra. Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from inception to June 27, 2023. Data pertaining to labral classification, surgical method, radiographic findings, and clinical outcomes were recorded. The quality of included studies was assessed by the Methodological Index for Non-Randomized Studies (MINORS) criteria. Results: Seven case series, eleven retrospective cohort studies, and two prospective cohort studies were included for analysis. Studies were divided into an irreparable labral group comprising 1,002 patients and a hypoplastic labral group comprising 935 patients. Treatments included repair, augmentation, or reconstruction. In the irreparable group, 12 studies recorded improvement of modified Harris Hip Score (mHHS) with preoperative scores ranging from 50.3–67.3 and postoperative scores from 76.2–95.0. The rate of conversion to total hip arthroplasty (THA) and rate of revision arthroscopy were 6.6% and 5.9%, respectively. In the hypoplastic group, two studies that focused on repair noted no statistical difference in mHHS for repair in hypoplastic vs. non-hypoplastic labrum. One study noted that labral repair in non-hypoplastic labra showed superior mHHS compared to hypoplastic labra (P<0.001). Conclusions: Treatment of irreparable labra with reconstruction or augmentation results in improved patient reported outcome measures (PROMs). For the hypoplastic labrum, primary repair results in improvement in PROMs. Future comparative studies focusing on hypoplastic labra alone, rather than irreparable labral tears are needed to properly assess patient outcomes and guide surgical indications.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.014 | 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".