Distal Ulna Fractures
Bibliographic record
Abstract
OBJECTIVE: To determine the biomechanical properties of plating options for distal ulna fractures. METHODS: Fourth-generation ulna artificial bones were osteotomized and fixed with 4 different constructs: 2 locking compression plates (a straight 2.7-mm plate and a 2.4-mm T-plate) with both nonlocking and locking screws. The artificial bones underwent nondestructive tests to determine construct stiffness in flexion/extension and lateral bending. The final testing consisted of cyclical loading in axial torsion until implant failure. RESULTS: The straight plate fixation construct was significantly stiffer than the T-plate construct for both flexion/extension bending (P < 0.001) and radial/ulnar bending (P < 0.05). Nonlocking screws provided significantly stiffer fixation in flexion bending than locking screws (P < 0.05); however, no difference was found in extension bending. Conversely, locking screws were significantly stiffer in radial/ulnar bending than the nonlocking screws (P < 0.05). Failure under torsional cyclical loading was significantly different among constructs. The straight plate with nonlocking construct withstood the most half-cycles. The mechanisms of failure were unique to each type of fixation. CONCLUSIONS: These results do not show any clear biomechanical advantage of locked plating for fractures of the distal ulna. The increased stiffness associated with locked plating likely contributes to earlier and more pronounced failure mechanisms under repetitive axial torsion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".