Design and validation of a cadaveric knee joint loading device compatible with magnetic resonance imaging and computed tomography
Bibliographic record
Abstract
PURPOSE: Design and validation of a magnetic resonance and computed tomography compatible device capable of applying physiologically relevant muscle forces to cadaveric knee joints with high levels of repeatability and reproducibility. METHODS: Repeatability and reproducibility were assessed with two porcine stifle joints. Load was applied to joints at full extension, five and 15 degrees of flexion through two cables simulating the lines of action of the quadriceps and hamstrings muscles. Five repeatability and five reproducibility trials were performed at each flexion angle. Standard deviations (SDs) of joint angle and load were recorded. RESULTS: For repeatability, the maximum SDs for joint angle were 1.26° (flexion), 1.54° (ab/adduction) and 0.90° (in/external rotation). The maximum SDs for joint load were 4.60 N (anterior/posterior), 7.36 N (medial/lateral), and 42.6N (axial). For reproducibility, the maximum SDs for joint angle were 0.84° (flexion), 0.66° (ab/adduction) and 0.92° (in/external rotation). The maximum SDs for joint load were 6.40 N (anterior/posterior), 11.7 N (medial/lateral), and 39.7 N (axial). CONCLUSIONS: This level of repeatability and reproducibility is within intra-subject variability of measured gait kinematics. Therefore, this device is considered to be an effective tool for in vitro testing of knee soft tissue repair.
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 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".