EP1.38 Labral debridement and labral repair in isolated labral pathology of the hip: do both work?
Bibliographic record
Abstract
Abstract Introduction: The arthroscopic treatment of femoroacetabular impingement (FAI) with bony resection and labral repair or debridement is effective. However, high rates of failure and conversion to THA are reported in the treatment of symptomatic labral tears with isolated arthroscopic debridement. Symptomatic isolated labral pathology occurs, often secondary to significant trauma. The efficacy of labral repair or debridement in this population is less clear. Methods: We retrospectively reviewed the outcome of labral repair and debridement in 102 patients undergoing hip arthroscopy between 2018-2021. Exclusion criteria included Cam resection and revision arthroscopy. Acetabular bony resection was not excluded as this can occur in labral repair. 30 cases of isolated labral pathology with labral repair and 28 cases with labral debridement were identified. Patient-reported outcome measures (PROMs) were analyzed. Results: Both groups were well-matched for age, sex, BMI and duration of follow-up. Average age at surgery for repair was 36 (range 20-55) and 33 for debridement (range 21-56). There were 27 females and 3 males in the repair group and 27 females and 1 male in the debridement group. Where recorded, average BMI at surgery was 26.9 (repair) and 26.6 (debridement). Average duration of follow-up was 21.1 months (range 1-43) for repair and 22.4 months (range 5-40) for debridement. Average pre-op iHOT-12 score was 32.8 for repair (range 1.27-77.8, n=24) and 28.5 (range 1.3-69.4, n=25) for debridement. At 3 months post-op, average iHOT-12 score was 53.8 (range 6.4-99.8, n=19) in the repair group and 58.2 (range 10.8-96.9, n=13) in the debridement group. At 12 months post-op, average iHOT-12 score was 57.8 (range 3.8-94.4, n=10) with repair and 63.3 (range 15-98, n=13) with debridement. Conclusion: Labral repair and labral debridement are effective treatments for isolated labral pathology in the short to medium-term. At 3 and 12 months, a greater improvement in i-HOT-12 score was observed with labral debridement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".