The effect of radial head fracture size on elbow kinematics and stability
Bibliographic record
Abstract
This study determined the effect of radial head fracture size and ligament injury on elbow kinematics. Eight cadaveric upper extremities were studied in an in vitro elbow simulator. Testing was performed with ligaments intact, with the medial collateral (MCL) or lateral collateral (LCL) ligament detached, and with both the MCL and LCL detached. Thirty degree wedges were sequentially removed from the anterolateral radial head up to 120 degrees . Valgus angulation and external rotation of the ulna relative to the humerus were determined for passive motion, active motion, and pivot shift testing with the arm in a vertical (dependent) orientation. Maximum varus-valgus laxity was calculated from measurements of varus and valgus angulation with the arm in horizontal gravity-loaded positions. No effect of increasing radial head fracture size was observed on valgus angulation during passive and active motion in the dependent position. In supination, external rotation increased with increasing fracture size during passive motion with LCL deficiency and both MCL and LCL deficiency. With intact ligaments, maximum varus-valgus laxity increased with increasing radial head fracture size. With ligament disruption, elbows were grossly unstable, and no effect of increasing radial head fracture size occurred. During pivot shift testing, performed with the ligaments intact, subtle instability was noted after resection of one-third of the radial head. In this in vitro biomechanical study, small subtle effects of radial head fracture size on elbow kinematics and stability were seen in both the ligament intact and ligament deficient elbows. These data suggest that fixation of displaced radial head fractures less than or equal to one-third of the articular diameter may have some biomechanical advantages; however, clinical correlation is required.
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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 | 0.000 | 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) | 0.002 | 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".