Knee joint kinematics and cartilage health using magnetic resonance imaging: applications to high tibial osteotomy
Bibliographic record
Abstract
Osteoarthritis (OA) is a prevalent disease with mechanical risk factors. One risk factor, varus knee alignment, is associated with medial tibiofemoral (TF) OA. High tibial osteotomy (HTO) is a surgical treatment for younger patients with varus malalignment that aims to reduce medial TF loading by realigning the mechanical axis. Post-HTO MR investigation of cartilage health is complicated by metal artifact from surgical implants. Delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) is a validated assessment of cartilage health sensitive to early OA. Techniques to reduce metal artifact in dGEMRIC were tested in phantom and in vivo. Saturation recovery reduced the extent of metal artifact, allowing dGEMRIC measurement near metal. The mechanical change caused by HTO may increase lateral TF or patellofemoral (PF) loads, which may damage cartilage. Fourteen knees were examined before and after HTO using dGEMRIC. No significant differences were found between pre-operative and either 6- or 12-month results (TF or PF). These results indicate that cartilage may not be degenerating in the short term with HTO. Clinical measures of mechanical changes with HTO are often frontal radiographs obtained in one joint position. Three-dimensional kinematic changes associated with HTO are unknown. Using a validated MR kinematics method, fourteen knees were examined before and after HTO. Seven of 11 kinematic parameters (TF and PF) showed significant differences between pre-operative and both 6- and 12-month follow-ups. These 3D changes may relate to clinical success; identifying these relationships may lead to improvements in HTO. Knee kinematics are often assessed from a series of static positions. However, differences may exist between kinematics estimated from static poses and those from movement. A new dynamic method was developed to evaluate differences between static and dynamic kinematics in normal knees (n = 10). Eight of 11 kinematic parameters showed significant differences between dynamic and static kinematics. Dynamic 3D kinematics are often different from static results, and may provide information not obtainable from static scans. In conclusion, numerous changes in knee joint kinematics and no apparent changes in cartilage health are associated with HTO within one year. Methods developed may help answer important questions about other orthopaedic disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".