Revision Arthroscopic Bankart Repair Versus Arthroscopic Latarjet for Failed Primary Arthroscopic Stabilization With Subcritical Bone Loss
Bibliographic record
Abstract
BACKGROUND: Limited evidence is available regarding the recommended technique of revision surgery for recurrent shoulder instability. Only 1 previous study has compared the results of soft tissue repair and the Latarjet technique in patients with persistent shoulder instability after primary surgical stabilization. PURPOSE/HYPOTHESIS: To evaluate the results of revision surgery in patients with previous surgical stabilization failure and subcritical glenoid bone defects, comparing repeated Bankart repair versus arthroscopic Latarjet technique. The hypothesis was that Latarjet would be superior to soft tissue procedures in terms of objective and subjective functional scores, recurrence rates, and range of movement. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Included were 45 patients (mean age, 29.1 ± 8.9 years) with subcritical bone loss (<15% of articular surface) who had undergone revision anterior shoulder instability repair after failed Bankart repair. Of these, 17 patients had arthroscopic Bankart repair and 28 had arthroscopic Latarjet surgery. Patients were evaluated at a minimum of 2 years postoperatively with the Rowe score, Western Ontario Shoulder Instability Index, and Subjective Shoulder Value. Subluxation or dislocation episodes were considered failures. RESULTS: No statistically significant differences were found between groups in age, sex, sporting activity, preoperative Rowe score, or the presence of hyperlaxity or bony lesions. At revision arthroscopy, 20 shoulders showed a persistent Bankart lesion, 13 a medially healed labrum, and 6 a bony Bankart. In 6 patients, no abnormalities were present that could explain postoperative recurrence. In the Bankart repair group, 7 patients underwent isolated Bankart procedures; in the remaining 10 cases, a capsular shift was added. No significant differences were found between the Bankart and Latarjet groups in outcome scores, recurrence rate (11.8% vs 17.9%, respectively), or postoperative athletic activity level. The mean loss of passive external rotation at 0° and 90° of abduction was similar between groups. CONCLUSION: Arthroscopic Latarjet did not lead to superior results compared with repeated Bankart repair in patients with subcritical glenoid bone loss and recurrent anterior shoulder instability after Bankart repair.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".