MR imaging methods to study meniscal position and mechanics
Bibliographic record
Abstract
Objective: . In particular, magnetic resonance imaging (MRI) provides the three-dimensional (3D) visualization of the menisci required to make important measurements related to mechanical function. This mini review summarizes MR approaches that have been used to make measurements related to meniscal mechanics, including morphology, position, movement, shape, and extrusion. Design: A literature search was performed using PubMed and Google Scholar, with search terms including "meniscus" and "MRI" in combination with "mechanics", "position", "shape", "movement", "size", "loaded", and "unloaded". Articles were manually reviewed and selected by consensus between the authors as constituting the most important examples of work required to illustrate the breadth of measurement and imaging approaches used in research on meniscal function. Results: MRI has been used for quantitative 3D analyses of the morphology and position of the menisci. Morphological analyses included measurements of meniscal volume, thickness, width, and bulging. Positional analyses included measurements of the overlap between the meniscal surface and tibial joint surface, the amount of extrusion, and the percentage of joint surface area and meniscal area that were covered or uncovered. Open MR scanners have been used to measure the movement of the menisci from full extension to deep flexion. MR compatible loading devices have been used to study the effect of loading on meniscal morphology and extrusion. Studies using these methods have found that there are differences in meniscal morphology between healthy and osteoarthritic participants, that the lateral meniscus and anterior horns have greater movement throughout flexion, and that meniscal extrusion increases under load. Conclusions: MRI has improved our insight into meniscal mechanics. Simulated weightbearing, open imaging through the range of knee flexion, and image processing to yield 3D measurements have all contributed to this progress. These approaches have strong potential to explore clinically motivated research questions related to meniscal mechanics.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".