Radiographic Evaluation of Carpal Mechanics and the Scapholunate Angle in a Clenched Fist with Dynamic Computed Tomography Imaging
Bibliographic record
Abstract
Purpose: The long-term consequences of injuries to the scapholunate joint can severely limit hand function, and the potential for posttraumatic deformity makes early recognition of these injuries important. The purpose of this study was to evaluate the motion of the scapholunate joint in normal wrists through the radial and ulnar deviation using novel dynamic computed tomography (CT) imaging. Methods: Fifteen participants consented to have their uninjured wrists scanned. A protocol was designed to ensure adequate time, yet limited exposure, for volunteers. Participants began with the hand in a relaxed fist position and then proceeded to clench the hand in a full fist and relax. Once relaxed again, the wrist was maximally ulnarly deviated and then maximally radially deviated in a fluid motion. Dynamic CT imaging was captured throughout the range of motion. Results: The scapholunate angle was measured on dynamic wrist images. The mean range of the scapholunate angle that the wrists moved through was 37.2°-45.9°, and the mean midpoint angle was 41.2° ± 0.4°. All wrists had small, measurable differences in the scapholunate angle when moving from the maximum ulnar deviation to the maximal radial deviation. The average maximum angle change through the range is 11.7°, whereas the average minimum angle change was 0.9°. Conclusions: In this study, scapholunate angle calculations using dynamic wrist CT scans were within the range of accepted normal for the angle in uninjured wrists. With the increased focus on dynamic imaging for wrist motion, it may be possible to derive a standardized protocol for mapping the carpal motion that is clinically applicable and reproducible. Type of study/level of evidence: Diagnostic IV.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".