Glenolabral Articular Disruption (GLAD) Is Not Associated with Worse Outcomes or Higher Instability Recurrence after Arthroscopic Bankart Repair—A Matched-Pair Analysis
Bibliographic record
Abstract
Background: This study aimed to compare clinical outcomes and recurrence of instability after arthroscopic Bankart repair (ABR) in patients with anterior shoulder instability, with and without a GLAD lesion, while distinguishing between primary and recurrent instability. Methods: Consecutive patients who underwent isolated ABR between January 2012 and December 2021 were included. Patients with a concomitant GLAD lesion were matched in with patients without a GLAD lesion according to the following criteria: age, sex, BMI, follow-up time, and primary versus recurrent instability. At minimum two-year follow-up, the clinical outcome (Rowe score, redislocation rate) and the functional outcome, including the American Shoulder and Elbow Surgeons (ASES) score, Western Ontario Shoulder Instability Index (WOSI), Oxford Shoulder Instability Score (OSIS), satisfaction (1–10 scale, 0 = unsatisfied, 10 = very satisfied), and Visual Analogue Scale (VAS), were compared between groups. Results: In total, 28 patients (14 GLAD vs. 14 Bankart; age: 32.5 ± 13.0 years; sex: 92.9% male; BMI: 24.6 ± 2.2) were included 6.9 ± 2.8 (2–11) years after isolated ABR (follow-up rate 63.6%). Clinical and functional outcome did not differ significantly between patients with versus without GLAD lesions (ASES score: 100 [96.5–100] vs. 97.5 [93.3–100], p = 0.27); WOSI (%): 9.0 [3.7–24.5] vs. 3.8 [0.8–8.9], p = 0.22; Rowe score: 90.0 [75.0–100] vs. 95.0 [78.8–100], p = 0.57; OSIS: 46 [44.7–48] vs. 46 [43.0–48], p = 0.54; satisfaction: 8.9 ± 1.4 vs. 8.0 ± 1.4, p = 0.78; VAS 0 [0–1.3] vs. 0 [0–1.0]. In both groups, two patients (14.3%) reported a redislocation during the observation period. Conclusions: At short- to mid-term follow-up, ABR showed favorable outcomes, low dislocation rates, and high patient satisfaction, regardless of the presence of a GLAD lesion or primary versus recurrent instability. However, follow-up time was heterogeneous, and the follow-up rate was marginal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".