Paper 10: 5-Year Outcomes: Arthroscopic Anatomic Glenoid Augmentation is Superior to Bankart Repair
Bibliographic record
Abstract
Objectives: Arthroscopic anatomic glenoid reconstruction (AAGR) is an increasingly popular treatment approach for shoulder instability. AAGR shows a promising short-term safety profile and clinicoradiological outcomes, suggesting its efficacy is comparable to Bankart repair. Recurrence rates reported in the literature are up to 30% following Bankart repair, however, there is a lack of literature comparing the long-term outcomes of AAGR versus Bankart repair. The purpose was to compare recurrence rates and patient-reported outcomes in patients who received AAGR to patients who received Bankart repair with a minimum of 5-year follow-up. Methods: This was a retrospective study comparing 73 patients who underwent AAGR and 76 patients who underwent Bankart repair consecutively between 2012 and 2018. Patients who had a minimum 5-year follow-up were included in the final analysis. Data collected included demographics, pre- and postoperative Western Ontario Shoulder Instability (WOSI) Index and Disabilities of the Arms, Shoulders, and Hands (DASH) Scores, postoperative complications and recurrence. The primary outcome measure was dislocation recurrence, with secondary outcome measures of patient-reported outcomes and postoperative complications. Results: The demographics and preoperative outcome scores were similar between groups. Patients in the AAGR group presented with significantly higher bone loss (>10%, p<0.05) than those in the Bankart group. There were fewer incidences of recurrence in the AAGR group (1.4%) than the Bankart group (18.4%). There was a significant difference in pre-to-post operative WOSI (p=0.02) and DASH (p=0.01) scores between groups, as well as a significant difference in the number of patients who met the WOSI (p<0.001) and the DASH (p<0.001) minimum clinically important difference threshold, with the AAGR group showing a greater improvement pre- to postoperatively. Conclusions: AAGR has better long-term outcomes and fewer incidences of recurrence compared to 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".