Alignment differs between patellofemoral osteoarthritis cases and matched controls: An upright 3D MRI study
Bibliographic record
Abstract
Patellofemoral (PF) osteoarthritis (OA) is a prevalent and clinically important knee OA subgroup. Malalignment may be an important risk factor for PF OA. However, little is known about alignment in PF OA, particularly in an upright, weightbearing environment. Using a vertically-oriented open-bore MR scanner, we evaluated 3D knee alignment in 15 PF OA cases and 15 individually matched asymptomatic controls. We imaged one knee per participant while they stood two-legged at four flexion angles (0°, 15°, 30°, 45°), and also while they stood one-legged at 30° knee flexion. We calculated 3D patellofemoral and tibiofemoral alignment. Using mixed effects models, four of the five patellofemoral measures differed by group. For key measures, PF OA patellae were 6.6° [95%CI 5.0, 8.2] more laterally tilted, 2.4 mm [1.3, 3.5] more laterally translated, and at least 3.7 mm [0.2, 7.2] more proximally translated compared to controls (more with knees flexed). Alignment did not differ between two-legged stance and one-legged stance in either group. Statement of Clinical Significance: Our study demonstrated significant and clinically relevant differences in alignment between PF OA cases and controls in upright standing and squatting positions. Our findings were similar to those in previous studies of PF OA using traditional MR scanners in supine positions, supporting the clinical usefulness of existing methods aimed at identifying individuals who may benefit from interventions designed to correct malalignment. © 2019 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society. 9999:1-9, 2019.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".