Patello‐femoral tracking in the weight‐bearing knee: a study of asymptomatic volunteers utilising dynamic magnetic resonance imaging: a preliminary report
Bibliographic record
Abstract
Normal patello-femoral tracking is not well defined, and conventional radiological techniques do not allow imaging in the physiological, weight-bearing stance. A vertical-access open configuration magnetic resonance scanner allows imaging of patello-femoral tracking during weight-bearing and through a wide range of knee flexion. We imaged 40 asymptomatic knees in this way, producing axial scans which were analysed qualitatively and quantitatively using sulcus angle, congruence angle, lateral patello-femoral angle and patellar centralisation, to assess patellar tilt and displacement. Mild lateral tilting in hyperextension with the quadriceps relaxed was seen, but quantitative assessment of this was impeded by internal rotation of the femur in extension. One-half of the knees were slightly laterally displaced in hyper-extension, becoming central during the first 30 degrees of knee flexion. During passive flexion of the knee in a seated position, fewer knees were laterally tilted or displaced, and no consistent change was seen during flexion. These results indicate that mild lateral tilting and displacement can be normal phenomena in the weight-bearing knee in early flexion and should not necessarily be taken as evidence of abnormal tracking in symptomatic patients. Lateral to medial movement of the patella occurs during normal knee flexion. In addition, imaging in the weight-bearing knee can provide valuable information not gained by imaging during passive knee flexion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".