Radial Head Subluxation After Malalignment of the Proximal Ulna
Bibliographic record
Abstract
OBJECTIVES: Understanding the anatomy of proximal ulna is important when treating complex injuries, since nonanatomic reconstruction may lead to malunion, arthrosis, and instability. The proximal ulna has a sagittal bow, termed the proximal ulna dorsal angulation (PUDA). The purpose of this study was to evaluate the magnitude of angular malalignment at the PUDA that would lead to radial head subluxation. METHODS: This biomechanical study was conducted on 6 fresh frozen upper extremities with an elbow movement simulator. An osteotomy was performed at the PUDA and stabilized with internal fixation at 5 angles. Lateral elbow fluoroscopic images were taken in 4 elbow and 3 forearm positions, with the annular ligament intact and then released. The displacement of the radial head was quantified with the radiocapitellar ratio measurement. RESULTS: A significant interaction exists between elbow positions, angles of malalignment, and annular ligament integrity (P < 0.001). The greatest magnitudes of radial head subluxation were with annular ligament tear, ranging from -4% to 88% (P < 0.001). Significant differences were found between the different internal fixation angles (P = 0.002) and elbow positions (P < 0.001). Anterior subluxation increased as malalignment was fixed into extension and with progressive elbow flexion. Posterior subluxation increased as malalignment advanced into flexion and decreased with elbow flexion. CONCLUSIONS: Proximal ulna malalignment combined with an annular ligament tear affect the biomechanics of the elbow and can lead to radial head subluxation. This study demonstrates the importance of an anatomic reconstruction, specifically recreation of each individual's unique PUDA. Thus, in the setting of a comminuted proximal ulna fracture with associated annular ligament insufficiency, radiographs of the contralateral elbow may assist with the restoration of the normal anatomy to limit radial head instability.
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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 | 0.000 |
| 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 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".