In‐vivo patellar tracking in individuals with patellofemoral pain and healthy individuals
Bibliographic record
Abstract
Understanding of the exact cause of patellofemoral pain has been limited by methodological challenges to evaluate in-vivo joint motion. This study compared six degree-of-freedom patellar motion during a dynamic lunge task between individuals with patellofemoral pain and healthy individuals. Knee joints of eight females with patellofemoral pain and ten healthy females were imaged using a CT scanner in supine lying position, then by a dual-orthogonal fluoroscope while they performed a lunge. To quantify patellar motion, the three-dimensional models of the knee bones, reconstructed from CT scans, were registered on the fluoroscopy images using the Fluomotion registration software. At full knee extension, the patella was in a significantly laterally tilted (PFP: 11.77° ± 7.58° vs. healthy: 0.86° ± 4.90°; p = 0.002) and superiorly shifted (PFP: 17.49 ± 8.44 mm vs. healthy: 9.47 ± 6.16 mm, p = 0. 033) position in the patellofemoral pain group compared with the healthy group. There were also significant differences between the groups for patellar tilt at 45°, 60°, and 75° of knee flexion, and for superior-inferior shift of the patella at 30° flexion (p ≤ 0.031). In the non-weight-bearing knee extended position, the patella was in a significantly laterally tilted position in the patellofemoral pain group (7.44° ± 6.53°) compared with the healthy group (0.71° ± 4.99°). These findings suggest the critical role of passive and active patellar stabilizers as potential causative factors for patellar malalignment/maltracking. Future studies should investigate the associations between patellar kinematics with joint morphology, muscle activity, and tendon function in a same sample for a thorough understanding of the causes of patellofemoral pain. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.000 |
| 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 | 0.001 |
| 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".