Patients Have a 15% Redislocation Rate After Arthroscopic Bankart Repair With a Knotless Technique
Bibliographic record
Abstract
Purpose To evaluate the redislocation rate after arthroscopic Bankart repair (ABR) with a standardized knotless anchor technique in a consecutive series of patients with anterior glenohumeral instability. Methods Inclusion criteria were patients who underwent ABR by a single surgeon between January 2008 and December 2018 with a minimum follow up of 2 years. We collected data through phone interviews, Western Ontario Shoulder Instability Index, and review of patient records. The primary outcome was redislocation and secondary outcomes were recurrent subluxations, reoperation, postoperative complications, patient satisfaction, and functional outcomes. We also performed survival analysis and risk factor analysis. Results Of 88 patients (91 shoulders) who underwent ABR during the inclusion period, 70 patients (73 shoulders) were included (follow‐up rate 80%). The mean follow‐up was 7.5 years (range 2‐12 years). Redislocation occurred in 15% (95% confidence interval [CI] 7.8%‐25.4%) of patients at a mean of 41 months after surgery (range 6‐115 months). The reoperation rate for recurrent redislocation was 4.1%. Overall, 90.4% reported being currently satisfied with their shoulder and the mean Western Ontario Shoulder Instability Index score at follow‐up was 73.8% (range 8.3%‐99.9%). Patients with redislocation were younger at primary operation than patients with no redislocation (mean 21 years vs 28 years; P = .023) and adjusted hazard ratio for age was 0.86 (95% CI 0.74‐0.99; P = .033). It was more common to have less than 3 anchors in patients with redislocation ( P = .024), but adjusted hazard ratio was 4.42 (95% CI 0.93‐21.02; P = .061). Conclusions The redislocation rate after ABR with a standardized knotless anchor technique in a consecutive series of patients with anterior glenohumeral instability was found to be 15% after a minimum 2‐year follow‐up (mean 7.5). Level of Evidence Level IV, therapeutic case‐series.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".