<i>Editorial Commentary</i> : Suture Button Fixation for the Latarjet Procedure Is Superior to Screw Fixation
Bibliographic record
Abstract
An increasing body of evidence suggests that suture button fixation is comparable with screw fixation in Latarjet and potentially associated with reduced graft resorption. Suture button fixation may facilitate performing the Latarjet procedure, particularly when done in an arthroscopic manner. The use of suture button fixation technique theoretically facilitates improved positioning of the graft on the glenoid, as it frees the surgeon from the potential of the soft-tissue envelope to impact positioning of the graft, which can occur with screw-based guides. The bone graft can be shuttled into an optimal position and then tensioned with relative ease in comparison with screw-based techniques. Suture button fixation results in lower complication rates compared with screw fixation; almost one third of the screw fixation complications are hardware-related, and screw fixation results in a high rate of hardware removal. Moreover, the arthroscopic Latarjet suture button literature is published by experienced surgeons. Arthroscopic Latarjet has a significant learning curve, reducing the translatability of studies that report superior findings with any one technique. The generalizability of results reminds us that evidence-based medicine should be practiced through the lens of not only patient preferences, but also through an honest appraisal of a surgeon's own ability.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".