Arthroscopic Bankart Repair for Anterior Glenohumeral Instability in 488 Adolescents Between 2000 and 2020: Risk Factors for Subsequent Recurrent Instability Requiring Revision Stabilization
Bibliographic record
Abstract
BACKGROUND: After arthroscopic Bankart repair (ABR) for anterior glenohumeral instability (GHI), adolescent athletes have higher rates of subsequent recurrent GHI than any other subpopulation. Elucidating which adolescents are at highest risk of postoperative recurrent GHI may optimize surgical decision-making. PURPOSE: To identify prognostic factors associated with subsequent recurrent GHI requiring revision stabilization surgery (RSS) after ABR. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: The study included patients 12 to 21 years old who had undergone ABR for anterior GHI at a pediatric tertiary care hospital by 1 of 5 sports medicine fellowship-trained surgeons between 2000 and 2020. A multivariate Cox proportional hazards model, with percentage of patients with recurrent GHI undergoing subsequent RSS, was used with a time-to-event outcome analysis. The Cox model effects were expressed as the hazard ratio (HR). All tests were 2-sided, with an alpha of .05. RESULTS: = .01). Adolescents with only 1 preoperative dislocation had a cumulative incidence of RSS (3.2%), which was significantly lower than those with 2 (24.2%) or ≥3 preoperative dislocations (33.5%). CONCLUSION: The number of dislocations before index ABR was the strongest risk factor for recurrent GHI requiring RSS in adolescents with anterior GHI, with 2 dislocations conferring >7-fold increased risk compared with a single preoperative dislocation. Other significant risk factors included the presence of a Hill-Sachs lesion, younger age, and participation in contact sports.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".