The Knee Loading Apparatus: Axial, Anterior, and Compressive Loading With Magnetic Resonance Imaging
Bibliographic record
Abstract
When magnetic resonance (MR) images are collected while applying a load to the knee joint, additional information about the joint response to loading can be acquired such as cartilage deformation, whole joint and ligament stiffness, or physiological estimates of weight-bearing joint positions. To allow load application and controlled lower limb movement in supine MR imaging, the knee loading apparatus (KLA) was designed to apply safe and physiologically relevant controlled loads to the knee joint, position the knee through a range of flexion angles, and operate successfully in a magnetic environment. The KLA is composed of three main components: a remotely operated custom hydraulic loading system, a logic system that interfaces with the user, and modular non ferromagnetic positioning frames. Three positioning frames are presented for application to anterior tibial loading, tibiofemoral compression, and patellofemoral compression at multiple knee flexion angles. This system design makes improvements over current devices. Safe remotely applied loads (hydraulic loading system) can be applied by either subject or tester and in multiple locations simultaneously. Additionally, loads can be altered at any time in a continuous manner without electrical interference. Transportability was improved due to a smaller footprint. The KLA has the flexibility to attach any positioning frame with many possible loading scenarios without changing the loading mechanism or logic systems, and allows force values over time to be output rather than estimated. An evaluation of the load repeatability (within 7% of applied load) and accuracy (0.5–14.9%) demonstrates the feasibility of this design for investigations into in vivo knee joint responses to loading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".