Patellofemoral and tibiofemoral alignment in a fully weight‐bearing upright MR: Implementation and repeatability
Bibliographic record
Abstract
PURPOSE: To develop methods for evaluating 3D patellofemoral and tibiofemoral alignment in vertical open-bore magnetic resonance (MR) scanners, with participants upright and fully weight-bearing; and to evaluate the repeatability of these methods in individuals with patellofemoral osteoarthritis (OA) and in asymptomatic knees. MATERIALS AND METHODS: -weighted turbo spin echo images in a 3T scanner to create accurate participant-specific 3D anatomical surface models. In a vertical open-bore 0.5T MR scanner, we obtained lower-resolution sagittal gradient echo images to capture bony position and orientation data. Participants were scanned in a position of squatting with the knees flexed 30°, three separate times to evaluate repeatability. Bone segmentation was performed manually, surface models were registered to data from the 0.5T scanner, and 3D patellofemoral and tibiofemoral alignment was calculated in all six degrees of freedom (three rotations and three translations). RESULTS: Intraclass correlation coefficients (ICCs) were ≥0.94, with the exception of patellar spin (0.79). Standard errors of measure (SEM) were <2° rotation and <0.9 mm translation. Repeatability remained adequate when stratified by group, with the exception of patellar spin (ICC 0.57 for asymptomatic knees vs. 0.91 for OA knees). CONCLUSION: We demonstrate methods for evaluating 3D alignment in upright fully weight-bearing participant positions in a vertical open-bore MR scanner. With the exception of patellar spin, repeatability was good to excellent. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:841-847.
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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 | 0.001 | 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".