Delayed Migration of Greater Tuberosity Fractures Associated With Anterior Shoulder Dislocation
Bibliographic record
Abstract
OBJECTIVES: Treatment of greater tuberosity (GT) fractures occurring during anterior shoulder dislocation generally consists of initial closed reduction of the shoulder. Undisplaced fractures are treated conservatively, whereas displaced fractures generally undergo surgical management. Our hypothesis is that many well-reduced GT fractures will migrate. The primary goal of this study is to evaluate the outcomes of GT fractures associated with shoulder dislocation to optimize their management. DESIGN: Retrospective review with prospective outcomes. SETTING: Tertiary Level 1 trauma center. PATIENTS: A total of 55 patients with anterior shoulder dislocation and GT fractures were identified. A matched cohort of isolated GT fractures was also identified. INTERVENTION: Closed reduction versus open reduction with or without fixation. OUTCOME MEASUREMENTS: Radiographs were evaluated for initial displacement, reduction, Hill-Sachs lesion, and subsequent displacement. Patients were evaluated using the Constant and quick DASH scores at a minimum of 1 year of follow-up. RESULTS: A majority of patients received initial closed reduction, with acceptable reduction of the tuberosity in 85%. With closed reduction, migration of the GT was seen in 9 cases (16%). In younger patients (<70 years), the failure rate increased to 26%. Displacement of the GT was 5.6 times more likely with dislocation than without. Patients with migration were only operated on in 33% of cases, mostly because of late presentation. CONCLUSIONS: Treatment of GT fractures occurring with anterior shoulder dislocation is complex. Although outcomes with nonoperative treatment are generally acceptable, a significant proportion of these patients will have tuberosity migration, which may impact function. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".