Dual fluoroscopic evaluation of human tibiofemoral joint kinematics during a prolonged standing: A pilot study
Bibliographic record
Abstract
A complete knowledge of tibiofemoral joint kinematics is essential for understanding the function of the healthy and pathological joint. The objective of the present study was to establish a dual fluoroscopic measurement protocol and a data processing approach for the creep response of the knee joint in order to further evaluate the mechanical properties of articular cartilage and meniscus in vivo. A computational approach was developed for the determination of 3D translations and rotations of the joints of young participants with no history of injury using dual fluoroscopic images of loaded joints and joint geometry reconstructed from magnetic resonance imaging of the unloaded joints. High-resolution X-ray images were obtained for the distal femur and proximal tibia during 10-min standing when approximately ¾ body weight was slowly applied to the right leg and then kept constant for the rest duration of the test. Anatomic coordinate systems were established for the 3D models of distal femur and proximal tibia. Translations and rotations of the joint as functions of time were then evaluated using the X-ray images and these coordinate systems with the JointTrack software. The displacements in the proximal-distal direction obtained from two participants were consistent, showing a substantial increase in the initial phase when joint loading increased from nil to ¾ body weight and a continued small increase over time while the joint loading remained constant. The maximum anterior-posterior translations during 10-min standing were approximately 4 mm for both participants, although one showed better stability than the other. In conclusion, a creep loading protocol of the knee joint can be reasonably established for in vivo conditions and evaluated with the image-based computational approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".