Long-term outcomes following isolated arthroscopic Bankart repair: a 9- to 12-year follow-up
Bibliographic record
Abstract
BACKGROUND: The long-term outcomes following arthroscopic Bankart repair have been rarely reported. Because of its relative novelty, little is known about recurrent instability, postoperative arthritis, and patient satisfaction, particularly for well-established modern procedures. The purpose of the study was to evaluate the long-term outcomes following arthroscopic Bankart repair. METHODS: Patients who underwent isolated arthroscopic Bankart repair from 2003 to 2006 were retrospectively reviewed. Recurrent instability, radiographic, and clinical scores (American Shoulder and Elbow Surgeons [ASES], Simple Shoulder Test [SST], and Rowe scores) were evaluated. Patient factors (ie, age, gender, side, number of instability episodes, contact sports, and bone loss) were analyzed to determine the correlation with outcome measures. RESULTS: Among the 98 patients (102 shoulders), we were able to contact 50 patients (51 shoulders, mean age 27.0 years, mean follow-up 121.2 months). Significant bone loss in glenoid and humerus was arthroscopically observed in 16 (31.4%) and 28 (54.9%) shoulders, respectively. Sixteen shoulders (31.4%) experienced recurrent instability. Recent radiographs were obtained for 38 shoulders, 14 (36.8%) of which showed moderate to severe arthritis. Clinical outcomes at follow-up were 89.3, 10.8, and 76.0 for ASES, SST, and Rowe scores, respectively. Neither recurrent instability nor arthritis was correlated with any patient factors. CONCLUSION: When isolated arthroscopic Bankart repair was used in all patients with shoulder instability regardless of bony defect, postoperative recurrent instability and arthritis rates were unacceptably high. Additional procedures should be chosen after careful consideration of multiple patient factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".