The Impact of Scaphoid Malunion on Carpal Motion: An In-Vitro Analysis
Bibliographic record
Abstract
Background: The clinical significance of scaphoid malunion is debated and its effect on wrist kinematics is poorly understood. Alterations to scaphoid morphology in other pathologies result in significant sequelae and arthrosis. By understanding the impact of scaphoid malunion on carpal kinematics, better insight can be garnered to inform models of wrist motion and clinical treatment of this injury. Methods: Seven cadaveric upper extremities underwent active wrist flexion and extension in a custom motion wrist simulator with scaphoid kinematics being captured with respect to the distal radius. A three-stage protocol of progressive simulated malunion severity was performed [intact, 10° malunion (Mal 10°), 20° malunion (Mal 20°)] with data analyzed from 45° wrist extension to 45° wrist flexion. Scaphoid malunions were modelled by creating successive volar wedge osteotomies and reducing the resultant scaphoid fragments with two 0.062” Kirshner wires. Motion of the scaphoid, lunate, trapezium-trapezoid was recorded using optical trackers. Results: Increasing scaphoid malunion severity did not significantly affect scaphoid or trapezium-trapezoid motion (p > 0.05); however, it did significantly alter lunate motion (p < 0.001). Increasing malunion severity resulted in progressive lunate extension across wrist motion (Intact – Mal 10°: mean difference (mean dif.) = 7.1° ± 1.6, p < 0.05; Intact – Mal 20°: mean dif. = 10.2° ± 2.0, p < 0.05). Conclusions: In this in-vitro model, increasing scaphoid malunion severity was associated with progressive extension of the lunate in all wrist positions. The clinical significance of this motion change is yet to be elucidated, but this model serves as a basis for understanding the kinematic consequences of scaphoid malunion deformities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".