NON-RIGID FIXATION RESULTS IN HIGHER FAILURE RATES COMPARED WITH SCREWS FOLLOWING ARTHROSCOPIC GLENOID RECONSTRUCTION
Bibliographic record
Abstract
It has previously been established that screw fixation results in better outcomes than button fixation for arthroscopic anatomic glenoid reconstruction (AAGR) with frozen allograft. There has been considerable debate on the causes for the higher dislocation rates with non-rigid fixation. The purpose of this study was to evaluate which factors may affect failure rates for AAGR with non-rigid fixation. This was a retrospective review of patients who had AAGR using non-rigid fixation that were age-, gender-, and followup-matched with screw fixation patients. Arthroscopic videos were used to evaluate surgical factors including graft placement, fixation quality, tensioning, graft contact, soft tissue quality, quality of soft tissue repair, presence of Hill-Sachs, and addition of Remplissage. Patient demographic factors (age, gender, medical history etc) were also assessed. XRays and CTs were used to evaluate graft union, screw and button angle, graft remodeling, and button pull through. A total of 36 patients were included in our study who had AAGR between 2014 and 2019 (18 non-rigid fixation matched to 18 screw fixation). Seven patients who had non-rigid fixation had a dislocation requiring reoperation. Of these, 4/7 had poor anterior soft tissue. All but one patient had excellent soft tissue repair and balance at end of surgery. All seven patients had complete graft resorption compared with those did not fail who had a normal post-operative glenoid A-P dimension. In our case series, AAGR with non-rigid fixation had high rate of recurrent dislocation. These failed patients had appropriate surgical factors that were equivalent between dislocated patients and successful patients. The failure rate was not correlated with surgical technique, patient or radiographic factors but rather only fixation type. Further study is needed to evaluate fixation methods and to assess whether type of graft impacts these clinical outcomes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".