A meta‐analysis of open versus arthroscopic Bankart repair using suture anchors
Bibliographic record
Abstract
Purpose of this study is to conduct a meta-analysis comparing the results of open and arthroscopic Bankart repair using suture anchors in recurrent traumatic anterior shoulder instability. Using Medline Pubmed, Cochrane and Embase databases we performed a search of all published articles. We included only studies that compared open and arthroscopic repair using suture anchors. Statistical analysis was performed using chi-square test. Six studies met the inclusion criteria. The total number of patients was 501, 234 suture anchors and 267 open. The rate of recurrent instability in the arthroscopic group was 6% versus 6.7% in the open group; rate of reoperation was 4.7% in the arthroscopic group vs. 6.6% in open (difference not statistically significant). The difference was statistically significant only in the studies after 2002 (2.9% of recurrence in the arthroscopic group vs. 9.2% in open; 2.2% of reoperation in the arthroscopic group vs. 9.2% in open). Results regarding function couldn't be combined because of non-homogeneous scores reported in the original articles, but the arthroscopic treatment led to better functional results. Arthroscopic repair using suture anchors results in similar redislocation and reoperation rate compared to open Bankart repair; however, we need larger and more homogeneous prospective studies to confirm these findings.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.056 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".