Long-term outcomes of the Bankart and Latarjet repairs: a systematic review
Bibliographic record
Abstract
Abstract: The most common surgical techniques for the treatment of recurrent anterior shoulder instability include the arthroscopic Bankart repair, the open Bankart repair and the open Latarjet procedure. The purpose of this study was to evaluate and compare the long-term outcomes following these procedures. A systematic review of modern procedures with a minimum follow-up of 5 years was completed. The objective outcome measures evaluated were post-operative dislocation and instability rate, the Rowe score, radiographic arthritis and complications. Twenty-eight studies with a total of 1652 repairs were analyzed. The estimated re-dislocation rate was 15.1% following arthroscopic Bankart repair, 7.7% following open Bankart repair and 2.7% following Latarjet repair, with the comparison between arthroscopic Bankart and open Latarjet reaching statistical significance ( p <0.001). The rates of subjective instability and radiographic arthritis were consistently high across groups, with no statistical difference between groups. Estimated complication rates were statistically higher in the open Latarjet repair (9.4%) than in the arthroscopic Bankart (0%; p =0.002). The open Latarjet procedure yields the most reliable method of stabilization but the highest complication rate. There are uniformly high rates of post-operative subjective instability symptoms and radiographic arthritis at 5 years regardless of procedure choice. Keywords: Bankart, Latarjet, long-term outcomes, instability
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".