Empirical joint contact mechanics: A comprehensive review
Bibliographic record
Abstract
Empirical joint contact mechanics measurement (EJCM; e.g. contact area or force, surface velocities) enables critical investigations of the relationship between changing joint mechanics and the impact on surface-to-surface interactions. In orthopedic biomechanics, understanding the changes to cartilage contact mechanics following joint pathology or aging is critical due to its suggested role in the increased risk of osteoarthritis (OA), which might be due to changed kinematics and kinetics that alter the contact patterns within a joint. This article reviews and discusses EJCM approaches that have been applied to articulating joints such that readers across different disciplines will be informed of the various measurement and analysis techniques used in this field. The approaches reviewed include classical measurement approaches (radiographic and sectioning, dye staining, casting, surface proximity, and pressure measurement), stereophotogrammetry/motion analysis, computed tomography (CT), magnetic resonance imaging (MRI), and high-speed videoradiography. Perspectives on approaches to advance this field of EJCM are provided, including the value of considering relative velocity in joints, tractional stress, quantification of joint contact area shape, consideration of normalization techniques, net response (superposition) of multiple input variables, and establishing linkages to regional cartilage health status. EJCM measures continue to provide insights to advance our understanding of cartilage health and degeneration and provide avenues to assess the efficacy and guide future directions of developing interventions (e.g. surgical, biological, rehabilitative) to optimize joint's health and function long term.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".