Do dynamic‐based MR knee kinematics methods produce the same results as static methods?
Bibliographic record
Abstract
MR-based methods provide low risk, noninvasive assessment of joint kinematics; however, these methods often use static positions or require many identical cycles of movement. The study objective was to compare the 3D kinematic results approximated from a series of sequential static poses of the knee with the 3D kinematic results obtained from continuous dynamic movement of the knee. To accomplish this objective, we compared kinematic data from a validated static MR method to a fast static MR method, and compared kinematic data from both static methods to a newly developed dynamic MR method. Ten normal volunteers were imaged using the three kinematic methods (dynamic, static standard, and static fast). Results showed that the two sets of static results were in agreement, indicating that the sequences (standard and fast) may be used interchangeably. Dynamic kinematic results were significantly different from both static results in eight of 11 kinematic parameters: patellar flexion, patellar tilt, patellar proximal translation, patellar lateral translation, patellar anterior translation, tibial abduction, tibial internal rotation, and tibial anterior translation. Three-dimensional MR kinematics measured from dynamic knee motion are often different from those measured in a static knee at several positions, indicating that dynamic-based kinematics provides information that is not obtainable from static scans.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".