High long‐term failure rates after arthroscopic Bankart repair in younger patients with recurrent shoulder dislocations: A plea for early treatment
Bibliographic record
Abstract
PURPOSE: To determine arthroscopic Bankart repair outcomes and recurrence risk factors at a minimum 5-year follow-up. METHODS: Retrospective assessment of prospectively collected data, single-cohort study of patients who underwent arthroscopic Bankart repair with a minimum 5-year follow-up. Demographical and preoperative instability features were collected. Primary outcome was recurrent instability set as dislocation or subluxation. Secondary outcomes were revision surgery, postoperative instability degree according to Manta criteria, objective and subjective clinical and functional status, assessed by the Rowe, Western Ontario Shoulder Index (WOSI) and Subjective Shoulder Value (SSV) scores. Return to sport and postoperative sports activity at the final follow-up were also recorded. RESULTS: One-hundred and seventy-two patients, 82% men, average age at surgery 29.5 ± 9.2 years, were included. At a mean follow-up of 8.3 ± 2.6 years, recurrent instability occurred in 53 of 172 patients (30.8%). Revision surgery was required in 23/53 (43.4%) of shoulder with recurrent instability. Recurrence occurred within the first 2 years postoperative in 49% of the shoulders, whereas 51% of recurrences occurred after this period. Recurrence took place after a traumatic event in 25% and 56%, respectively. Recurrence rates were higher in patients who underwent surgery after two or more dislocations (p = 0.029). Patients younger at the time of first dislocation, younger at surgery and those with a higher preoperative degree of instability also showed significantly higher rates of recurrence (p = 0.04, p = 0.02, p = 0.03). Postoperative ROWE, WOSI and SSV scores were significantly worse in patients with recurrent instability (p < 0.001). Return-to-sports rate was also lower in patients with postoperative recurrence (p < 0.001). CONCLUSION: The arthroscopic Bankart repair was associated with a high long-term recurrence rate, and its effectiveness decreased over time. The lowest recurrence rates in arthroscopic Bankart repair were achieved in older patients with only one prior instability episode and a lower instability degree. LEVEL OF EVIDENCE: Level IV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".