A Kinematic Analysis of Wrist and Carpal Function Using Four-Dimensional Computed Tomography Technology: A Dynamic Perspective
Bibliographic record
Abstract
PURPOSE: An emerging imaging modality, four-dimensional computed tomography, can provide dynamic evaluation of carpal motion, which allows for a better understanding of how the carpals work together to achieve range of motion. The objective of this work was to examine kinematic motion of the carpus through a flexion/extension arc of motion using four-dimensional computed tomography. METHODS: A convenience sample of 20 uninjured participants underwent a four-dimensional computed tomography scanning protocol through a complete arc of flexion/extension motion. Kinematic changes in motion were quantified using helical axes motion data for each carpal. Rotation angles were compared between bones to identify differences in kinematic motion between bones. RESULTS: The bones within the proximal carpal row, the lunate, scaphoid, and triquetrum, rotate significantly to differing magnitudes at the ends of motion (40° of flexion and 40° of extension). The scaphoid rotates to the highest magnitude, followed by the triquetrum, and lastly, the lunate. The distal carpal row bones rotate to similar magnitudes throughout the entire range of motion. CONCLUSIONS: This work describes the kinematics of the carpals throughout dynamic in vivo flexion and extension. CLINICAL RELEVANCE: This study adds to an understanding of wrist mechanics and the possible clinical implications of pathological deviation from baseline kinematics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".