Risk Factors for Recurrence After Arthroscopic Instability Repair—The Importance of Glenoid Bone Loss >15%, Patient Age, and Duration of Symptoms: A Matched Cohort Analysis
Bibliographic record
Abstract
Background: Glenoid bone loss (GBL) has been implicated as a risk factor for failure of arthroscopic anterior glenohumeral instability repair. Although certain amounts of GBL are associated with higher recurrence rates, there are limited studies on successes versus failures in these cohorts. Purpose: To compare the outcomes of arthroscopic Bankart repair in patients with and without GBL to determine a threshold percentage of GBL that predicts success. Study Design: Cohort study; Level of evidence, 2. Methods: All consecutive patients who underwent arthroscopic Bankart repair for anterior shoulder instability between 2004 and 2013 were prospectively enrolled. Patients with ≤25% GBL were included. Patients with no GBL were grouped and compared with those having 5% to 25% GBL. Outcomes included Single Assessment Numerical Evaluation, Western Ontario Shoulder Index, and American Shoulder and Elbow Surgeons scores, with evidence of recurrent instability. Patients with and without GBL were statistically compared with respect to outcomes and recurrence rates. Results: Of 434 eligible patients, the cases of 405 (45 female, 360 male; mean age, 27.5 years [range, 18-47 years]) were followed for a mean 61 months (range, 48-96 months). There were 189 (46.6%) with no GBL and 216 (53.3%) with GBL; the mean GBL of the latter cohort was 15% (range, 5%-25%). The mean duration of instability symptoms was 7.9 months (range, 1-21 months) and was significantly longer in the GBL group ( P < .05). The mean recurrence rate was 14.8%, which was significantly greater in patients presenting with GBL versus those with none (48/216 [22.2%] vs 12/189 [6.3%]; P < .01). Within the GBL group, GBL ≥15%, duration of symptoms >5 months, and younger age (<20 years) were independent risk factors for failure ( P < .01). Patients with any GBL had >4-times greater odds of recurrence after arthroscopic stabilization (odds ratio, 4.21; 95% CI, 2.16-8.21). Moreover, patients presenting for arthroscopic Bankart repair with GBL ≥15% had nearly 3-times greater odds of recurrent instability. Conclusion: GBL ≥15% in an active patient population portends to increased odds of recurrent instability events and inferior clinical outcomes after arthroscopic Bankart repair. Furthermore, nonmodifiable risk factors, such as age (<20 years) and duration of symptoms before presentation (>5 months), significantly affect risk of recurrence and should be key factors when counseling patients on risk of failure and determining the ideal procedure for the individual patient.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".