Short‐term Complications of the Arthroscopic Latarjet Procedure: A North American Experience
Bibliographic record
Abstract
PURPOSE: To report on the intraoperative and early postoperative (<3 months) problems and complications encountered with the arthroscopic Latarjet procedure in patients with complex anterior shoulder instability. METHODS: Between 2010 and 2014, 83 patients underwent an arthroscopic Latarjet procedure for recurrent post-traumatic anterior instability. The group's mean age was 28 ± 10 years and consisted of 76 (92%) male patients. A "problem" was defined as an unanticipated perioperative event that was not likely to affect the patient's final outcome. A "complication" was defined as an event that was likely to negatively affect outcome. RESULTS: At a mean follow-up of 17 months (range, 3 to 43 months), 20 (24%) patients sustained either a problem and/or a complication. The problem rate was 18% and the complication rate was 10%. The most commonly encountered adverse event was intraoperative fracture of the coracoid graft, which occurred in 6 patients (7%). In addition, 1 arthroscopic case was intraoperatively converted to open and 1 patient sustained a transient axillary nerve injury. A total of 7 cases underwent secondary operative procedures. The rate of problems and/or complications in primary cases was not significantly different than revision cases (P = .335). CONCLUSIONS: The rate of adverse events reported in this arthroscopic series is not insignificant and is similar to that reported with the traditional open Latarjet. With appropriate training, the arthroscopic Latarjet procedure can be effective for the management of patients with complex shoulder instability. LEVEL OF EVIDENCE: Level IV, therapeutic case series.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".