Surgical management and results of glenohumeral combination fractures of the anterior glenoid rim and the proximal humerus
Bibliographic record
Abstract
INTRODUCTION: The combination of anterior large glenoid rim fractures (GRF) and proximal humerus fractures (PHF) is rare, with limited data available on specific treatments for these glenohumeral combination fractures (GCF). This study aimed to evaluate the treatment approaches for GCF, analyze patient outcomes, and outline surgical management strategies for different fracture types. MATERIALS AND METHODS: This retrospective study included patients with GCF, excluding those with fossa glenoidalis fractures, isolated greater tuberosity fractures, or small glenoid rim fractures (< 5 mm). Preoperative radiographs, CT scans, and follow-up radiographs were reviewed. Clinical outcomes were assessed using the Constant-Murley Score (CMS), Western Ontario Shoulder Instability Index (WOSI), Rowe Score (RS), and Oxford Shoulder Score (OSS). RESULTS: Sixteen patients with 17 GCFs (mean age 62 years) were followed for an average of 39 months. PHFs were categorized into three-part (76%), four-part (12%), and two-part fractures (12%). The average medial displacement of GRF was 5 mm, with an average dehiscence of 4 mm in the sagittal plane. Fourteen patients (88%) underwent surgical treatment; 35% had only the PHF surgically addressed, while 53% had both lesions surgically treated. Two patients (12%) received non-operative treatment. Complications were observed in 29% of cases, primarily involving the humeral side. The average CMS was 68 points, WOSI was 75%, RS was 77 points, and OSS was 41 points. CONCLUSION: Treating GCF is complex and routinely necessitates surgical intervention, with or without GRF refixation. CT imaging is crucial for precise assessment of fracture morphology. The involvement of the minor tuberosity is critical in selecting the optimal surgical approach and managing the subscapularis muscle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".