Hip Labral and Capsular Repair Are Unable to Restore Distractive Stability in a Biomechanical Model
Bibliographic record
Abstract
PURPOSE: To evaluate the change in hip distractive stability after a capsulotomy, labral tear, and simultaneous repair of both the capsule and the labrum in a biomechanical model. METHODS: Ten fresh-frozen human cadaveric hips were analyzed using a materials testing system to measure the distractive force and distance required to disrupt the hip suction seal under the following conditions: (1) native intact capsule and labrum, (2) 2- or 4-cm interportal capsulotomy (IPC), (3) labral tear, (4) T extension, (5) labral repair, (6) T extension repair, and (7) IPC repair. Each specimen was retested at 0° of flexion, 45° of flexion, and 45° of flexion with 15° of internal rotation. RESULTS: A significantly higher distractive force was required to rupture the suction seal in the intact condition compared with IPC (P = .012; 95% confidence interval [CI], 4.9-42.4); IPC and labral tear (P = .002; 95% CI, 11.3-49.4); IPC, labral tear, and T extension (P = .001; 95% CI, 13.9-51.5); IPC, labral repair, and T extension (P < .001; 95% CI, 20.8-49.7); IPC, labral repair, and T extension repair (P = .002; 95% CI, 12.5-52.4); and IPC repair, labral repair, and T extension repair (P = .01; 95% CI, 5.8-46.1). The IPC condition required a higher distractive force in isolation than when combined with a labral tear (P = .14; 95% CI, 1.2-12.0), T extension (P = .005; 95% CI, 2.8-15.3), or labral repair (P = .002; 95% CI, 4.4-18.8). CONCLUSIONS: The distractive resistance of an intact hip capsule and labrum was not restored once the soft tissues were violated, despite labral repair with a loop technique and capsular repair with interrupted figure-of-8 sutures. CLINICAL RELEVANCE: Time-zero complete capsular repair with concomitant labral repair may not be adequate to restore distractive hip stability after hip arthroscopy, reinforcing the use of postoperative precautions in the early postoperative period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".