Assessing sex differences in joint kinematics and ligament recruitment in CMC-1 OA patients: a preliminary study
Bibliographic record
Abstract
First carpometacarpal osteoarthritis (CMC-1 OA) is the most common form of OA in the hand, disproportionately affecting more women than men. The risk factors predisposing women to CMC-1 OA remain poorly understood. Fourdimensional CT (4DCT) imaging coupled with four-dimensional ultrasound (4DUS) imaging was utilized in this study to assess sex differences in joint kinematics and laxity. A male to female ratio of 1:2 CMC-1 OA patients were recruited for this preliminary study. 4DCT and 4DUS images were collected while patients performed primary thumb motions without loading on the joint. Kinematic models of the CMC-1 joint were developed to assess differences in joint kinematics between men and women. Ligament recruitment patterns at the thumb joint were assessed using our 4DUS system. The developed biomechanical models presented joint motion accurately throughout all performed motions. Current work is focused on evaluating the biomechanical risk factors that predispose women to CMC-1 OA. Anticipated results include assessing sex differences in CMC-1 joint bone morphology, the degree of joint centroid translation, joint space narrowing, as well as changes in joint congruency throughout each motion. In addition, changes in length of the dorsoradial ligament throughout thumb motion will be measured from the 4D ultrasound images collected. To the best of our knowledge, this is the first study utilizing 4DCT and 4DUS in tandem to assess thumb joint kinematics and ligament recruitment patterns. This work is a step forward in understanding the biomechanical factors and ligament recruitment patterns that result in women’s increased predisposition to the development of CMC-1 OA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".