Comparison of Computed Tomography Articular Surface Geometry of Male Versus Female Thumb Carpometacarpal Joints
Bibliographic record
Abstract
Background: Given the predilection of first carpometacarpal (CMC) joint osteoarthritis in women compared with men, we aim to determine the differences in first CMC joint surface geometry and congruence between young healthy males and females. Methods: Wrist computed tomographic scan data of 11 men and 11 women aged 20 to 35 years were imported into 3-dimensional software programs. The first metacarpal and the trapezium were aligned in a standardized position according to landmarks at key points on Gaussian and maximum curvature maps. Measurements of joint congruence and surface geometry were analyzed, including joint space volume, distance between the bones at the articular surface edges, area of the joint space, and radii of curvature in the radial-ulnar and volar-dorsal planes. Results: The mean thumb CMC articular space volume was 104.02 ± 30.96 mm 3 for females and 138.63 ± 50.36 mm 3 for males. The mean first metacarpal articular surface area was 144.9 ± 10.9 mm 2 for females and 175.4 ± 25.3 mm 2 for males. After normalizing for size, the mean thumb CMC articular space volume was 119.4 ± 24.6 mm 3 for females and 117.86 ± 28.5 mm 3 for males. There was also no significant difference for the articular space volume, articular surface distances, articular space, and mean radii of curvatures. Conclusions: This study found that there are sex differences in the first CMC joint articular volume without normalizing for size; however, there are no sex differences in first CMC joint articular volume, curvature characteristics, or joint congruence of young, healthy patients after normalizing for joint size.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".