Arthroscopic guided Latarjet
Bibliographic record
Abstract
Background: Anterior shoulder instability associated with glenoid bone deficiency is frequently managed via the Latarjet procedure. Although this coracoid transfer provides stabilization through a triple-locking mechanism—comprising a static bone block, a dynamic sling effect, and capsular repair—traditional screw fixation is associated with significant complications. These include graft malposition, hardware-related morbidity such as screw pullout or breakage, and potential neurovascular injury during arthroscopic or open implementation. Objective: This article describes a standardized arthroscopic Latarjet technique utilizing a guided approach and cortical-button fixation to improve the accuracy of graft placement and reduce complications associated with conventional metal screws. Key Points: The technique employs specific coracoid and glenoid guides to ensure the bone block is positioned flush to the glenoid surface and below the equator. Fixation is achieved using a double cortical-button device and a sliding-locking Nice knot, which provides compression while mitigating risks of graft resorption or hardware failure. The five-step surgical protocol includes coracoid and glenoid preparation, a subscapularis split, graft transfer, and a concomitant Bankart repair. Arthroscopic visualization enhances the safety of the procedure by allowing direct inspection of neurovascular structures and facilitating the treatment of associated labral or capsular pathology. Clinical data from 76 patients demonstrated accurate graft positioning, reproducible bone union at six months, and an absence of neurological or hardware-related complications. Conclusion: The guided arthroscopic Latarjet technique with suture-button fixation offers a reproducible alternative to traditional screw fixation. By optimizing graft orientation and eliminating rigid hardware, this method addresses technical challenges and improves safety in the management of recurrent anterior shoulder instability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".