Does labral treatment technique influence the outcome of FAI surgery? A matched-pair study of labral reconstruction versus repair and debridement with a follow-up of 10 years
Bibliographic record
Abstract
ABSTRACT The aim of this study was to analyze the long-term clinical outcomes of labral reconstruction in patients undergoing femoro-acetabular impingement (FAI) surgery and compare them with labral repair and debridement. This is a single-center, single-surgeon, retrospective match-paired study from a prospectively collected hip preservation database. All patients underwent a hip surgical dislocation for FAI surgery. Eight patients underwent labral reconstruction with the ligamentum teres and were matched on sex, age and body mass index with 24 labral repair and 24 labral debridement (1:3). Failure was defined as conversion to total hip replacement (THR) and patient-reported outcome measures (PROMs) were collected. Mean follow-up was 9.8 years ±2.6 (5.2–13.9). There was a significant improvement in postoperative PROMs in the three groups regarding the WOMAC total, WOMAC function, HOOS-QoL, HOOS-ADL and HOOS-SRA (P < 0.05). There was no statistical difference between the three groups regarding postoperative PROMs and change in PROMs (P > 0.05). A total of 10 hips underwent joint replacement surgery at a mean time of 7.9 ± 3.5 years (2.4–12). There was no statistically significant difference between the three groups regarding the conversion rate to THR (P = 0.64) or time between surgery and conversion to THR (P = 0.15). Compared to a match-pair group of labral repair and debridement, labral reconstruction with ligamentum teres provides similar survival with conversion to a THR as an endpoint, as well as similar improvement in PROMs. Labral treatment can be safely adapted at the nature of the labral lesion with a treatment ‘à la carte’.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".