Radiological method for measuring patellofemoral tracking and tibiofemoral kinematics before and after total knee replacement
Bibliographic record
Abstract
OBJECTIVES: Numerous complications following total knee replacement (TKR) relate to the patellofemoral (PF) joint, including pain and patellar maltracking, yet the options for in vivo imaging of the PF joint are limited, especially after TKR. We propose a novel sequential biplane radiological method that permits accurate tracking of the PF and tibiofemoral (TF) joints throughout the range of movement under weightbearing, and test it in knees pre- and post-arthroplasty. METHODS: A total of three knees with end-stage osteoarthritis and three knees that had undergone TKR at more than one year's follow-up were investigated. In each knee, sequential biplane radiological images were acquired from the sagittal direction (i.e. horizontal X-ray source and 10° below horizontal) for a sequence of eight flexion angles. Three-dimensional implant or bone models were matched to the biplane images to compute the six degrees of freedom of PF tracking and TF kinematics, and other clinical measures. RESULTS: The mean and standard deviation for the six degrees of freedom of PF tracking and TF kinematics were computed. TF and PF kinematics were highly accurate (< 0.9 mm, < 0.6°) and repeatable. CONCLUSIONS: The developed method permitted measuring of in vivo PF tracking and TF kinematics before and after TKR throughout the range of movement. This method could be a useful tool for investigating differences between cohorts of patients (e.g., with and without pain) impacting clinical decision-making regarding surgical technique, revision surgery or implant design.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".